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                    <h1 class="text-lg md:text-xl font-bold text-gray-800">arXiv 每日论文精选</h1>
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                        <i class="fa fa-calendar-o mr-1"></i>2025-11-19
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                    <span class="text-gray-500 mr-1"><i class="fa fa-file-text-o"></i> 总论文数:</span>
                    <span id="total-papers" class="font-semibold text-primary">156</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-star"></i> 精选论文数:</span>
                    <span id="selected-papers" class="font-semibold text-accent">16</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-line-chart"></i> 平均评分:</span>
                    <span id="avg-score" class="font-semibold text-secondary">2.1</span>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14403v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>边训练边推理：一种用于点击率预测的掩码生成对称范式
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Infer As You Train: A Symmetric Paradigm of Masked Generative for Click-Through Rate Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Moyu Zhang, Yujun Jin, Yun Chen, Jinxin Hu, Yu Zhang, Xiaoyi Zeng
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何克服CTR预测中传统判别式范式的局限性，核心思想是建立训练与推理对称的掩码生成范式，在推理阶段利用生成能力迭代重构输入特征以提升预测准确性。</p>
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出训练与推理对称的生成范式，直接应用于CTR预测这一核心推荐系统任务，解决了传统判别式方法的局限性，与用户关注的直接LLM应用和核心领域进展高度相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 12:07:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14403v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14403v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Generative models are increasingly being explored in click-through rate (CTR) prediction field to overcome the limitations of the conventional discriminative paradigm, which rely on a simple binary classification objective. However, existing generative models typically confine the generative paradigm to the training phase, primarily for representation learning. During online inference, they revert to a standard discriminative paradigm, failing to leverage their powerful generative capabilities to further improve prediction accuracy. This fundamental asymmetry between the training and inference phases prevents the generative paradigm from realizing its full potential. To address this limitation, we propose the Symmetric Masked Generative Paradigm for CTR prediction (SGCTR), a novel framework that establishes symmetry between the training and inference phases. Specifically, after acquiring generative capabilities by learning feature dependencies during training, SGCTR applies the generative capabilities during online inference to iteratively redefine the features of input samples, which mitigates the impact of noisy features and enhances prediction accuracy. Extensive experiments validate the superiority of SGCTR, demonstrating that applying the generative paradigm symmetrically across both training and inference significantly unlocks its power in CTR prediction.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14221v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>面向本地生活推荐的LLM对齐地理项目标记化方法
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            LLM-Aligned Geographic Item Tokenization for Local-Life Recommendation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hao Jiang, Guoquan Wang, Donglin Zhou, Sheng Yu, Yang Zeng, Wencong Zeng, Kun Ga...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究本地生活推荐中地理信息建模不足的问题，核心思想是通过强化学习对齐LLM的地理知识，并采用分层tokenization策略将空间属性和语义内容统一编码为离散ID。</p>
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对推荐系统应用LLM技术，提出了地理信息与LLM对齐的创新方法，并设计了分层tokenization策略，完全符合核心领域进展和直接LLM应用的研究重点。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 07:54:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14221v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14221v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in Large Language Models (LLMs) have enhanced text-based recommendation by enriching traditional ID-based methods with semantic generalization capabilities. Text-based methods typically encode item textual information via prompt design and generate discrete semantic IDs through item tokenization. However, in domain-specific tasks such as local-life services, simply injecting location information into prompts fails to capture fine-grained spatial characteristics and real-world distance awareness among items. To address this, we propose LGSID, an LLM-Aligned Geographic Item Tokenization Framework for Local-life Recommendation. This framework consists of two key components: (1) RL-based Geographic LLM Alignment, and (2) Hierarchical Geographic Item Tokenization. In the RL-based alignment module, we initially train a list-wise reward model to capture real-world spatial relationships among items. We then introduce a novel G-DPO algorithm that uses pre-trained reward model to inject generalized spatial knowledge and collaborative signals into LLMs while preserving their semantic understanding. Furthermore, we propose a hierarchical geographic item tokenization strategy, where primary tokens are derived from discrete spatial and content attributes, and residual tokens are refined using the aligned LLM's geographic representation vectors. Extensive experiments on real-world Kuaishou industry datasets show that LGSID consistently outperforms state-of-the-art discriminative and generative recommendation models. Ablation studies, visualizations, and case studies further validate its effectiveness.
                </div>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14182v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>WebRec：通过基于注意力引导的网页检索增强生成技术增强基于大语言模型的推荐系统
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            WebRec: Enhancing LLM-based Recommendations with Attention-guided RAG from Web
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zihuai Zhao, Yujuan Ding, Wenqi Fan, Qing Li
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何利用网络信息增强基于LLM的推荐系统性能，核心思想是设计WebRec框架，通过LLM将推荐任务转化为适合网络检索的查询，并采用消息传递注意力机制从噪声网络内容中提取相关信息。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接应用LLM技术于推荐系统，提出基于RAG的创新框架，并设计了新的注意力机制处理异构数据，完全契合核心关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 06:35:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14182v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14182v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recommender systems play a vital role in alleviating information overload and enriching users' online experience. In the era of large language models (LLMs), LLM-based recommender systems have emerged as a prevalent paradigm for advancing personalized recommendations. Recently, retrieval-augmented generation (RAG) has drawn growing interest to facilitate the recommendation capability of LLMs, incorporating useful information retrieved from external knowledge bases. However, as a rich source of up-to-date information, the web remains under-explored by existing RAG-based recommendations. In particular, unique challenges are posed from two perspectives: one is to generate effective queries for web retrieval, considering the inherent knowledge gap between web search and recommendations; another challenge lies in harnessing online websites that contain substantial noisy content. To tackle these limitations, we propose WebRec, a novel web-based RAG framework, which takes advantage of the reasoning capability of LLMs to interpret recommendation tasks into queries of user preferences that cater to web retrieval. Moreover, given noisy web-retrieved information, where relevant pieces of evidence are scattered far apart, an insightful MP-Head is designed to enhance LLM attentions between distant tokens of relevant information via message passing. Extensive experiments have been conducted to demonstrate the effectiveness of our proposed web-based RAG methods in recommendation scenarios.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14130v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>PRISM：面向金融检索的提示优化上下文系统建模
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chun Chet Ng, Jia Yu Lim, Wei Zeng Low
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究金融信息检索中的文档排序和片段排序问题，核心方法是结合精炼系统提示、上下文学习和轻量级多代理系统，通过模块化集成实现协调的评分行为建模。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接应用LLM技术解决检索系统问题，提出了训练免费的多组件集成框架，与搜索和推荐系统的LLM应用高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 04:30:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14130v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14130v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CE</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With the rapid progress of large language models (LLMs), financial information retrieval has become a critical industrial application. Extracting task-relevant information from lengthy financial filings is essential for both operational and analytical decision-making. The FinAgentBench dataset formalizes this problem through two tasks: document ranking and chunk ranking. We present PRISM, a training-free framework that integrates refined system prompting, in-context learning (ICL), and a lightweight multi-agent system. Each component is examined extensively to reveal their synergies: prompt engineering provides precise task instructions, ICL supplies semantically relevant few-shot examples, and the multi-agent system models coordinated scoring behaviour. Our best configuration achieves an NDCG@5 of 0.71818 on the restricted validation split. We further demonstrate that PRISM is feasible and robust for production-scale financial retrieval. Its modular, inference-only design makes it practical for real-world use cases. The source code is released at https://bit.ly/prism-ailens.
                </div>
            </details>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14137v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>基于卷积最近邻的注意力机制
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Attention Via Convolutional Nearest Neighbors
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mingi Kang, Jeová Farias Sales Rocha Neto
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究卷积神经网络和Transformer架构的根本统一问题，核心思想是将卷积和自注意力统一到k近邻聚合框架中，卷积基于空间邻近性选择邻居，而注意力基于特征相似性选择邻居，两者构成连续谱系。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文统一卷积和自注意力的框架直接推进Transformer架构效率和新注意力机制，对推荐系统和搜索中的序列建模具有重要启发价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 04:54:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14137v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14137v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The shift from Convolutional Neural Networks to Transformers has reshaped computer vision, yet these two architectural families are typically viewed as fundamentally distinct. We argue that convolution and self-attention, despite their apparent differences, can be unified within a single k-nearest neighbor aggregation framework. The critical insight is that both operations are special cases of neighbor selection and aggregation; convolution selects neighbors by spatial proximity, while attention selects by feature similarity, revealing they exist on a continuous spectrum. We introduce Convolutional Nearest Neighbors (ConvNN), a unified framework that formalizes this connection. Crucially, ConvNN serves as a drop-in replacement for convolutional and attention layers, enabling systematic exploration of the intermediate spectrum between these two extremes. We validate the framework's coherence on CIFAR-10 and CIFAR-100 classification tasks across two complementary architectures: (1) Hybrid branching in VGG improves accuracy on both CIFAR datasets by combining spatial-proximity and feature-similarity selection; and (2) ConvNN in ViT outperforms standard attention and other attention variants on both datasets. Extensive ablations on $k$ values and architectural variants reveal that interpolating along this spectrum provides regularization benefits by balancing local and global receptive fields. Our work provides a unifying framework that dissolves the apparent distinction between convolution and attention, with implications for designing more principled and interpretable vision architectures.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14256v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>PathMind：基于大型语言模型的知识图谱推理的检索-优先级排序-推理框架
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            PathMind: A Retrieve-Prioritize-Reason Framework for Knowledge Graph Reasoning with Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yu Liu, Xixun Lin, Yanmin Shang, Yangxi Li, Shi Wang, Yanan Cao
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究知识图谱推理中路径选择不当和检索效率低的问题，核心思想是通过语义感知路径优先函数识别重要推理路径，采用检索-优先化-推理范式指导LLM进行忠实推理。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了检索-优先化-推理框架，直接应用LLM进行知识图谱推理，属于LLM在搜索/推荐系统中的直接应用领域。其路径优先化机制和双阶段训练策略对复杂推理任务具有重要参考价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 08:45:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14256v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14256v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Knowledge graph reasoning (KGR) is the task of inferring new knowledge by performing logical deductions on knowledge graphs. Recently, large language models (LLMs) have demonstrated remarkable performance in complex reasoning tasks. Despite promising success, current LLM-based KGR methods still face two critical limitations. First, existing methods often extract reasoning paths indiscriminately, without assessing their different importance, which may introduce irrelevant noise that misleads LLMs. Second, while many methods leverage LLMs to dynamically explore potential reasoning paths, they require high retrieval demands and frequent LLM calls. To address these limitations, we propose PathMind, a novel framework designed to enhance faithful and interpretable reasoning by selectively guiding LLMs with important reasoning paths. Specifically, PathMind follows a "Retrieve-Prioritize-Reason" paradigm. First, it retrieves a query subgraph from KG through the retrieval module. Next, it introduces a path prioritization mechanism that identifies important reasoning paths using a semantic-aware path priority function, which simultaneously considers the accumulative cost and the estimated future cost for reaching the target. Finally, PathMind generates accurate and logically consistent responses via a dual-phase training strategy, including task-specific instruction tuning and path-wise preference alignment. Extensive experiments on benchmark datasets demonstrate that PathMind consistently outperforms competitive baselines, particularly on complex reasoning tasks with fewer input tokens, by identifying essential reasoning paths.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14258v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>熵引导的推理压缩
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Entropy-Guided Reasoning Compression
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hourun Zhu, Yang Gao, Wenlong Fei, Jiawei Li, Huashan Sun
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究大型推理模型推理链过长导致的效率瓶颈问题，核心思想是通过分析训练过程中的熵冲突现象，设计熵引导训练框架来平衡推理压缩与性能，实现高效推理。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的熵引导推理压缩方法直接解决LLM推理效率问题，其熵冲突分析和训练框架对搜索推荐系统中的长序列建模具有重要借鉴意义。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 08:48:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14258v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14258v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large reasoning models have demonstrated remarkable performance on complex reasoning tasks, yet the excessive length of their chain-of-thought outputs remains a major practical bottleneck due to high computation cost and poor deployability. Existing compression methods have achieved partial success but overlook a crucial phenomenon in the training process -- the entropy conflict. During compression training, entropy decreases, leading to shorter reasoning but limited exploration, while accuracy-oriented objectives increase entropy, lengthening reasoning chains. This can cause the model to get stuck in a local dilemma. Our analysis further reveals the origin of the entropy conflict: many high-entropy tokens are logical connectors that receive larger gradients and are encouraged under the performance objective, while the compression objective simultaneously penalizes these potentially redundant connectors. This opposing pressure creates a direct source of entropy conflict. To address these issues, we adopt an entropy-guided training framework. As entropy descends, the model is guided toward efficient reasoning by encouraging concise thought steps; as entropy rises, exploration is reinforced under the compact reasoning mode to improve robustness. Experiments on six mathematical benchmarks show that our method compresses reasoning length to 20% of the original while maintaining or even surpassing baseline accuracy. Code and models will be released publicly.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14181v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>利用深度大语言模型参与实现鲁棒的实体链接
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Harnessing Deep LLM Participation for Robust Entity Linking
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiajun Hou, Chenyu Zhang, Rui Meng
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何更充分地利用大语言模型解决实体链接任务。核心思想是构建一个将LLM深度集成到实体链接全流程的框架，并设计基于全局上下文的自验证机制来纠正预测错误和识别实体间关联关系。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出深度集成LLM到实体链接全流程的方法，并引入自验证机制增强实体间关系理解，直接适用于搜索领域的核心语义理解任务。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 06:35:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14181v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14181v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Entity Linking (EL), the task of mapping textual entity mentions to their corresponding entries in knowledge bases, constitutes a fundamental component of natural language understanding. Recent advancements in Large Language Models (LLMs) have demonstrated remarkable potential for enhancing EL performance. Prior research has leveraged LLMs to improve entity disambiguation and input representation, yielding significant gains in accuracy and robustness. However, these approaches typically apply LLMs to isolated stages of the EL task, failing to fully integrate their capabilities throughout the entire process. In this work, we introduce DeepEL, a comprehensive framework that incorporates LLMs into every stage of the entity linking task. Furthermore, we identify that disambiguating entities in isolation is insufficient for optimal performance. To address this limitation, we propose a novel self-validation mechanism that utilizes global contextual information, enabling LLMs to rectify their own predictions and better recognize cohesive relationships among entities within the same sentence. Extensive empirical evaluation across ten benchmark datasets demonstrates that DeepEL substantially outperforms existing state-of-the-art methods, achieving an average improvement of 2.6\% in overall F1 score and a remarkable 4% gain on out-of-domain datasets. These results underscore the efficacy of deep LLM integration in advancing the state-of-the-art in entity linking.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14169v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>AdaTok：面向高效多模态大语言模型的对象感知表征自适应令牌压缩
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            AdaTok: Adaptive Token Compression with Object-Aware Representations for Efficient Multimodal LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinliang Zhang, Lei Zhu, Hangzhou He, Shuang Zeng, Ourui Fu, Jiakui Hu, Zhengjia...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究多模态大语言模型中图像token数量爆炸导致计算效率低下的问题，核心思想是采用对象级token合并策略替代传统的逐块扫描token化方法，实现自适应token压缩。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出对象感知的token压缩方法，直接针对Transformer效率问题，与推荐系统中处理高维序列数据的核心挑战高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 06:12:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14169v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14169v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal Large Language Models (MLLMs) have demonstrated substantial value in unified text-image understanding and reasoning, primarily by converting images into sequences of patch-level tokens that align with their architectural paradigm. However, patch-level tokenization leads to a quadratic growth in image tokens, burdening MLLMs' understanding and reasoning with enormous computation and memory. Additionally, the traditional patch-wise scanning tokenization workflow misaligns with the human vision cognition system, further leading to hallucination and computational redundancy. To address this issue, we propose an object-level token merging strategy for Adaptive Token compression, revealing the consistency with human vision system. The experiments are conducted on multiple comprehensive benchmarks, which show that our approach averagely, utilizes only 10% tokens while achieving almost 96% of the vanilla model's performance. More extensive experimental results in comparison with relevant works demonstrate the superiority of our method in balancing compression ratio and performance. Our code will be available.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14461v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>多轮播图书推荐的有效多样化
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Effective Diversification of Multi-Carousel Book Recommendation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Daniël Wilten, Gideon Maillette de Buy Wenniger, Arjen Hommersom, Paul Lucassen,...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究多轮播图书推荐中的多样性不足问题，核心思想是在协同过滤基础上设计多种策略来有效提升推荐项目的多样性，平衡准确性与多样性需求。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对推荐系统多样性这一核心问题，提出了多轮播场景下的多样性增强方法，属于推荐系统领域的直接应用创新。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 13:03:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14461v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14461v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Using multiple carousels, lists that wrap around and can be scrolled, is the basis for offering content in most contemporary movie streaming platforms. Carousels allow for highlighting different aspects of users' taste, that fall in categories such as genres and authors. However, while carousels offer structure and greater ease of navigation, they alone do not increase diversity in recommendations, while this is essential to keep users engaged. In this work we propose several approaches to effectively increase item diversity within the domain of book recommendations, on top of a collaborative filtering algorithm. These approaches are intended to improve book recommendations in the web catalogs of public libraries. Furthermore, we introduce metrics to evaluate the resulting strategies, and show that the proposed system finds a suitable balance between accuracy and beyond-accuracy aspects.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14368v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>O3SLM：开放权重、开放数据和开放词汇的草图语言模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            O3SLM: Open Weight, Open Data, and Open Vocabulary Sketch-Language Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rishi Gupta, Mukilan Karuppasamy, Shyam Marjit, Aditay Tripathi, Anirban Chakrab...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何解决视觉语言模型理解抽象手绘草图的局限性问题，核心方法是构建大规模图像-草图-指令三元组数据集，并训练能够统一处理视觉和草图模态的视觉语言模型。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文开发的大规模多模态数据集构建方法和视觉语言模型训练框架，可直接类比于推荐系统中处理异构用户行为序列和上下文特征的统一建模需求。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 11:18:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14368v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14368v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    While Large Vision Language Models (LVLMs) are increasingly deployed in real-world applications, their ability to interpret abstract visual inputs remains limited. Specifically, they struggle to comprehend hand-drawn sketches, a modality that offers an intuitive means of expressing concepts that are difficult to describe textually. We identify the primary bottleneck as the absence of a large-scale dataset that jointly models sketches, photorealistic images, and corresponding natural language instructions. To address this, we present two key contributions: (1) a new, large-scale dataset of image-sketch-instruction triplets designed to facilitate both pretraining and instruction tuning, and (2) O3SLM, an LVLM trained on this dataset. Comprehensive evaluations on multiple sketch-based tasks: (a) object localization, (b) counting, (c) image retrieval i.e., (SBIR and fine-grained SBIR), and (d) visual question answering (VQA); while incorporating the three existing sketch datasets, namely QuickDraw!, Sketchy, and Tu Berlin, along with our generated SketchVCL dataset, show that O3SLM achieves state-of-the-art performance, substantially outperforming existing LVLMs in sketch comprehension and reasoning.
                </div>
            </details>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14299v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>DataSage：基于外部知识检索、多角色辩论和多路径推理的多智能体协作洞察发现系统
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            DataSage: Multi-agent Collaboration for Insight Discovery with External Knowledge Retrieval, Multi-role Debating, and Multi-path Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaochuan Liu, Yuanfeng Song, Xiaoming Yin, Xing Chen
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究自动化数据洞察发现的核心问题，其核心方法是构建多智能体框架，通过外部知识检索丰富分析背景、多角色辩论机制模拟不同分析视角、多路径推理提高代码和洞察准确性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出多智能体协作框架，通过外部知识检索、多角色辩论和多路径推理解决数据洞察问题，与推荐系统和搜索领域的多模态建模和深度分析高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 09:54:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14299v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14299v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.MA</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In today's data-driven era, fully automated end-to-end data analytics, particularly insight discovery, is critical for discovering actionable insights that assist organizations in making effective decisions. With the rapid advancement of large language models (LLMs), LLM-driven agents have emerged as a promising paradigm for automating data analysis and insight discovery. However, existing data insight agents remain limited in several key aspects, often failing to deliver satisfactory results due to: (1) insufficient utilization of domain knowledge, (2) shallow analytical depth, and (3) error-prone code generation during insight generation. To address these issues, we propose DataSage, a novel multi-agent framework that incorporates three innovative features including external knowledge retrieval to enrich the analytical context, a multi-role debating mechanism to simulate diverse analytical perspectives and deepen analytical depth, and multi-path reasoning to improve the accuracy of the generated code and insights. Extensive experiments on InsightBench demonstrate that DataSage consistently outperforms existing data insight agents across all difficulty levels, offering an effective solution for automated data insight discovery.
                </div>
            </details>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14691v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>基于突触可塑性的注意力机制即是一切：一种受生物学启发的脉冲神经形态Transformer
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Attention via Synaptic Plasticity is All You Need: A Biologically Inspired Spiking Neuromorphic Transformer
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kallol Mondal, Ankush Kumar
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何解决传统Transformer注意力机制在能效和硬件兼容性方面的局限性。核心思想是利用大脑中的脉冲时序依赖可塑性（STDP）机制替代点积注意力，将查询-键相关性嵌入突触权重中，实现真正的神经形态计算和内存计算。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出通过STDP机制实现注意力，属于Transformer架构效率优化的前沿研究，与推荐系统中大规模模型部署的能效需求高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:28:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14691v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14691v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.NE</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.ET</span><span class="category-tag">stat.ML</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Attention is the brain's ability to selectively focus on a few specific aspects while ignoring irrelevant ones. This biological principle inspired the attention mechanism in modern Transformers. Transformers now underpin large language models (LLMs) such as GPT, but at the cost of massive training and inference energy, leading to a large carbon footprint. While brain attention emerges from neural circuits, Transformer attention relies on dot-product similarity to weight elements in the input sequence. Neuromorphic computing, especially spiking neural networks (SNNs), offers a brain-inspired path to energy-efficient intelligence. Despite recent work on attention-based spiking Transformers, the core attention layer remains non-neuromorphic. Current spiking attention (i) relies on dot-product or element-wise similarity suited to floating-point operations, not event-driven spikes; (ii) keeps attention matrices that suffer from the von Neumann bottleneck, limiting in-memory computing; and (iii) still diverges from brain-like computation. To address these issues, we propose the Spiking STDP Transformer (S$^{2}$TDPT), a neuromorphic Transformer that implements self-attention through spike-timing-dependent plasticity (STDP), embedding query--key correlations in synaptic weights. STDP, a core mechanism of memory and learning in the brain and widely studied in neuromorphic devices, naturally enables in-memory computing and supports non-von Neumann hardware. On CIFAR-10 and CIFAR-100, our model achieves 94.35\% and 78.08\% accuracy with only four timesteps and 0.49 mJ on CIFAR-100, an 88.47\% energy reduction compared to a standard ANN Transformer. Grad-CAM shows that the model attends to semantically relevant regions, enhancing interpretability. Overall, S$^{2}$TDPT illustrates how biologically inspired attention can yield energy-efficient, hardware-friendly, and explainable neuromorphic models.
                </div>
            </details>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14460v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Agent-R1：通过端到端强化学习训练强大的LLM智能体
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mingyue Cheng, Jie Ouyang, Shuo Yu, Ruiran Yan, Yucong Luo, Zirui Liu, Daoyu Wan...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何有效应用强化学习训练LLM智能体进行环境交互；核心方法是系统化扩展MDP框架定义LLM智能体关键组件，并开发模块化强化学习训练框架Agent-R1。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM智能体训练方法，虽非直接应用于推荐系统，但其强化学习框架和智能体交互能力对搜索和广告领域的用户交互建模具有潜在迁移价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 13:03:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14460v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14460v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) are increasingly being explored for building Agents capable of active environmental interaction (e.g., via tool use) to solve complex problems. Reinforcement Learning (RL) is considered a key technology with significant potential for training such Agents; however, the effective application of RL to LLM Agents is still in its nascent stages and faces considerable challenges. Currently, this emerging field lacks in-depth exploration into RL approaches specifically tailored for the LLM Agent context, alongside a scarcity of flexible and easily extensible training frameworks designed for this purpose. To help advance this area, this paper first revisits and clarifies Reinforcement Learning methodologies for LLM Agents by systematically extending the Markov Decision Process (MDP) framework to comprehensively define the key components of an LLM Agent. Secondly, we introduce Agent-R1, a modular, flexible, and user-friendly training framework for RL-based LLM Agents, designed for straightforward adaptation across diverse task scenarios and interactive environments. We conducted experiments on Multihop QA benchmark tasks, providing initial validation for the effectiveness of our proposed methods and framework.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14166v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>选择性弱到强泛化
            </a>
        </h3>
        <span class="score-badge bg-blue-100 text-blue-800">
            <i class="fa fa-star mr-1"></i>4/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Selective Weak-to-Strong Generalization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hao Lang, Fei Huang, Yongbin Li
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究超人类模型对齐中弱监督数据质量不足的问题，核心方法是训练二元分类器识别强模型能回答的问题，使用自生成标签进行对齐，并通过图平滑方法优化弱标签。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出选择性弱到强泛化框架，通过识别强模型已知问题来优化监督学习，与推荐系统中处理噪声数据和提升模型鲁棒性的需求相关，但应用场景主要针对模型对齐而非直接面向推荐搜索广告。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 06:03:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14166v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14166v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Future superhuman models will surpass the ability of humans and humans will only be able to \textit{weakly} supervise superhuman models. To alleviate the issue of lacking high-quality data for model alignment, some works on weak-to-strong generalization (W2SG) finetune a strong pretrained model with a weak supervisor so that it can generalize beyond weak supervision. However, the invariable use of weak supervision in existing methods exposes issues in robustness, with a proportion of weak labels proving harmful to models. In this paper, we propose a selective W2SG framework to avoid using weak supervision when unnecessary. We train a binary classifier P(IK) to identify questions that a strong model can answer and use its self-generated labels for alignment. We further refine weak labels with a graph smoothing method. Extensive experiments on three benchmarks show that our method consistently outperforms competitive baselines. Further analyses show that P(IK) can generalize across tasks and difficulties, which indicates selective W2SG can help superalignment.
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</div>
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14749v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>视觉大语言模型是参与度分析中的优秀噪声处理工具
            </a>
        </h3>
        <span class="score-badge bg-blue-100 text-blue-800">
            <i class="fa fa-star mr-1"></i>4/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Vision Large Language Models Are Good Noise Handlers in Engagement Analysis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Alexander Vedernikov, Puneet Kumar, Haoyu Chen, Tapio Seppänen, Xiaobai Li
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究视频参与度分析中主观标签噪声限制模型性能的问题，核心方法是利用视觉大语言模型提取行为线索划分数据可靠性，并结合课程学习与软标签优化策略逐步处理模糊样本。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文核心是使用VLM处理视频参与度分析中的噪声标签问题，虽然涉及多模态数据处理，但主要应用于计算机视觉领域而非推荐系统或广告的核心场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 18:50:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14749v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14749v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Engagement recognition in video datasets, unlike traditional image classification tasks, is particularly challenged by subjective labels and noise limiting model performance. To overcome the challenges of subjective and noisy engagement labels, we propose a framework leveraging Vision Large Language Models (VLMs) to refine annotations and guide the training process. Our framework uses a questionnaire to extract behavioral cues and split data into high- and low-reliability subsets. We also introduce a training strategy combining curriculum learning with soft label refinement, gradually incorporating ambiguous samples while adjusting supervision to reflect uncertainty. We demonstrate that classical computer vision models trained on refined high-reliability subsets and enhanced with our curriculum strategy show improvements, highlighting benefits of addressing label subjectivity with VLMs. This method surpasses prior state of the art across engagement benchmarks such as EngageNet (three of six feature settings, maximum improvement of +1.21%), and DREAMS / PAFE with F1 gains of +0.22 / +0.06.
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            <a href="https://www.alphaxiv.org/abs/2511.14096v1" target="_blank" rel="noopener noreferrer">
                NeuroPath：神经生物学启发的路径跟踪与反思，用于语义连贯检索
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            NeuroPath: Neurobiology-Inspired Path Tracking and Reflection for Semantically Coherent Retrieval
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junchen Li, Rongzheng Wang, Yihong Huang, Qizhi Chen, Jiasheng Zhang, Shuang Lia...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种受神经生物学启发的检索方法，关注语义连贯性，这与搜索系统中的相关性排序有一定关联。然而，标题未明确表明该方法在推荐系统、搜索或广告中的具体应用，且神经生物学灵感可能过于领域特定，限制了其在主流工业场景的直接适用性。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 03:28:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14096v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14096v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
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                    Retrieval-augmented generation (RAG) greatly enhances large language models (LLMs) performance in knowledge-intensive tasks. However, naive RAG methods struggle with multi-hop question answering due to their limited capacity to capture complex dependencies across documents. Recent studies employ graph-based RAG to capture document connections. However, these approaches often result in a loss of semantic coherence and introduce irrelevant noise during node matching and subgraph construction. To address these limitations, we propose NeuroPath, an LLM-driven semantic path tracking RAG framework inspired by the path navigational planning of place cells in neurobiology. It consists of two steps: Dynamic Path Tracking and Post-retrieval Completion. Dynamic Path Tracking performs goal-directed semantic path tracking and pruning over the constructed knowledge graph (KG), improving noise reduction and semantic coherence. Post-retrieval Completion further reinforces these benefits by conducting second-stage retrieval using intermediate reasoning and the original query to refine the query goal and complete missing information in the reasoning path. NeuroPath surpasses current state-of-the-art baselines on three multi-hop QA datasets, achieving average improvements of 16.3% on recall@2 and 13.5% on recall@5 over advanced graph-based RAG methods. Moreover, compared to existing iter-based RAG methods, NeuroPath achieves higher accuracy and reduces token consumption by 22.8%. Finally, we demonstrate the robustness of NeuroPath across four smaller LLMs (Llama3.1, GLM4, Mistral0.3, and Gemma3), and further validate its scalability across tasks of varying complexity. Code is available at https://github.com/KennyCaty/NeuroPath.
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            <a href="https://www.alphaxiv.org/abs/2511.14275v1" target="_blank" rel="noopener noreferrer">
                不要只见树木不见森林：通过答案空间推理实现LLM的深度置信度估计
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Don't Miss the Forest for the Trees: In-Depth Confidence Estimation for LLMs via Reasoning over the Answer Space
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ante Wang, Weizhi Ma, Yang Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM置信度估计，这属于模型可靠性和校准范畴。虽然置信度估计在搜索和推荐系统中对结果排序和不确定性建模有潜在应用，但论文标题更侧重于NLP领域的置信度评估技术，而非直接针对RecSys/Search/Ads的核心问题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 09:09:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14275v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14275v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Knowing the reliability of a model's response is essential in application. With the strong generation capabilities of LLMs, research has focused on generating verbalized confidence. This is further enhanced by combining chain-of-thought reasoning, which provides logical and transparent estimation. However, how reasoning strategies affect the estimated confidence is still under-explored. In this work, we demonstrate that predicting a verbalized probability distribution can effectively encourage in-depth reasoning for confidence estimation. Intuitively, it requires an LLM to consider all candidates within the answer space instead of basing on a single guess, and to carefully assign confidence scores to meet the requirements of a distribution. This method shows an advantage across different models and various tasks, regardless of whether the answer space is known. Its advantage is maintained even after reinforcement learning, and further analysis shows its reasoning patterns are aligned with human expectations.
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            <a href="https://www.alphaxiv.org/abs/2511.14582v1" target="_blank" rel="noopener noreferrer">
                OmniZip：音频引导的动态令牌压缩，用于快速全模态大语言模型
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            <i class="fa fa-star mr-1"></i>3/10
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            OmniZip: Audio-Guided Dynamic Token Compression for Fast Omnimodal Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Keda Tao, Kele Shao, Bohan Yu, Weiqiang Wang, Jian liu, Huan Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态LLM中的令牌压缩技术，虽然涉及效率优化，但其核心应用场景是音频-语言多模态处理，与推荐系统、搜索或广告的直接关联较弱。虽然令牌压缩技术理论上可以应用于推荐系统中的序列建模效率提升，但论文的音频引导特性和全模态焦点使其主要适用于多媒体内容处理而非核心RecSys/Search/Ads领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 15:22:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14582v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14582v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Omnimodal large language models (OmniLLMs) have attracted increasing research attention of late towards unified audio-video understanding, wherein processing audio-video token sequences creates a significant computational bottleneck, however. Existing token compression methods have yet to accommodate this emerging need of jointly compressing multimodal tokens. To bridge this gap, we present OmniZip, a training-free, audio-guided audio-visual token-compression framework that optimizes multimodal token representation and accelerates inference. Specifically, OmniZip first identifies salient audio tokens, then computes an audio retention score for each time group to capture information density, thereby dynamically guiding video token pruning and preserving cues from audio anchors enhanced by cross-modal similarity. For each time window, OmniZip compresses the video tokens using an interleaved spatio-temporal scheme. Extensive empirical results demonstrate the merits of OmniZip - it achieves 3.42X inference speedup and 1.4X memory reduction over other top-performing counterparts, while maintaining performance with no training.
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            <a href="https://www.alphaxiv.org/abs/2511.14503v1" target="_blank" rel="noopener noreferrer">
                面向多任务密集预测的参数感知Mamba模型
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            <i class="fa fa-star mr-1"></i>3/10
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            Parameter Aware Mamba Model for Multi-task Dense Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinzhuo Yu, Yunzhi Zhuge, Sitong Gong, Lu Zhang, Pingping Zhang, Huchuan Lu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及Mamba架构（一种状态空间模型替代Transformer）和多任务学习，属于Transformer架构效率方面的进展。然而，论文聚焦于密集预测任务（如图像分割），与推荐系统、搜索或广告中的排序和检索任务相关性较弱。多任务学习在推荐系统中可能有应用，但需要更具体的连接。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 13:48:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14503v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14503v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Understanding the inter-relations and interactions between tasks is crucial for multi-task dense prediction. Existing methods predominantly utilize convolutional layers and attention mechanisms to explore task-level interactions. In this work, we introduce a novel decoder-based framework, Parameter Aware Mamba Model (PAMM), specifically designed for dense prediction in multi-task learning setting. Distinct from approaches that employ Transformers to model holistic task relationships, PAMM leverages the rich, scalable parameters of state space models to enhance task interconnectivity. It features dual state space parameter experts that integrate and set task-specific parameter priors, capturing the intrinsic properties of each task. This approach not only facilitates precise multi-task interactions but also allows for the global integration of task priors through the structured state space sequence model (S4). Furthermore, we employ the Multi-Directional Hilbert Scanning method to construct multi-angle feature sequences, thereby enhancing the sequence model's perceptual capabilities for 2D data. Extensive experiments on the NYUD-v2 and PASCAL-Context benchmarks demonstrate the effectiveness of our proposed method. Our code is available at https://github.com/CQC-gogopro/PAMM.
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            <a href="https://www.alphaxiv.org/abs/2511.14396v1" target="_blank" rel="noopener noreferrer">
                基于语义-物理对齐的连续视觉-语言-动作协同学习用于行为克隆
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Continuous Vision-Language-Action Co-Learning with Semantic-Physical Alignment for Behavioral Cloning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiuxiu Qi, Yu Yang, Jiannong Cao, Luyao Bai, Chongshan Fan, Chengtai Cao, Hongpe...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉-语言-动作的行为克隆，属于机器人控制和具身AI领域。虽然提及视觉-语言模型的概念，但其核心应用是物理行为控制而非推荐系统、搜索或广告。在推荐/搜索/广告场景中，用户行为序列建模通常不涉及物理动作执行，因此直接相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 12:01:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14396v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14396v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    Language-conditioned manipulation facilitates human-robot interaction via behavioral cloning (BC), which learns control policies from human demonstrations and serves as a cornerstone of embodied AI. Overcoming compounding errors in sequential action decisions remains a central challenge to improving BC performance. Existing approaches mitigate compounding errors through data augmentation, expressive representation, or temporal abstraction. However, they suffer from physical discontinuities and semantic-physical misalignment, leading to inaccurate action cloning and intermittent execution. In this paper, we present Continuous vision-language-action Co-Learning with Semantic-Physical Alignment (CCoL), a novel BC framework that ensures temporally consistent execution and fine-grained semantic grounding. It generates robust and smooth action execution trajectories through continuous co-learning across vision, language, and proprioceptive inputs (e.g., robot internal states). Meanwhile, we anchor language semantics to visuomotor representations by a bidirectional cross-attention to learn contextual information for action generation, successfully overcoming the problem of semantic-physical misalignment. Extensive experiments show that CCoL achieves an average 8.0% relative improvement across three simulation suites, with up to 19.2% relative gain in human-demonstrated bimanual insertion tasks. Real-world tests on a 7-DoF robot further confirm CCoL's generalization under unseen and noisy object states.
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            <a href="https://www.alphaxiv.org/abs/2511.14186v1" target="_blank" rel="noopener noreferrer">
                基于统一多实体图与蒸馏的少样本精确事件发现
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            Few-Shot Precise Event Spotting via Unified Multi-Entity Graph and Distillation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhaoyu Liu, Kan Jiang, Murong Ma, Zhe Hou, Yun Lin, Jin Song Dong
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注事件发现任务，这属于特定领域的事件检测问题，与推荐系统、搜索或广告的核心技术关联较弱。虽然蒸馏技术可能具有通用性，但论文标题未明确展示其在推荐/搜索/广告领域的潜在应用，且事件发现本身更偏向信息提取而非用户行为建模。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 06:45:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14186v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14186v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Precise event spotting (PES) aims to recognize fine-grained events at exact moments and has become a key component of sports analytics. This task is particularly challenging due to rapid succession, motion blur, and subtle visual differences. Consequently, most existing methods rely on domain-specific, end-to-end training with large labeled datasets and often struggle in few-shot conditions due to their dependence on pixel- or pose-based inputs alone. However, obtaining large labeled datasets is practically hard. We propose a Unified Multi-Entity Graph Network (UMEG-Net) for few-shot PES. UMEG-Net integrates human skeletons and sport-specific object keypoints into a unified graph and features an efficient spatio-temporal extraction module based on advanced GCN and multi-scale temporal shift. To further enhance performance, we employ multimodal distillation to transfer knowledge from keypoint-based graphs to visual representations. Our approach achieves robust performance with limited labeled data and significantly outperforms baseline models in few-shot settings, providing a scalable and effective solution for few-shot PES. Code is publicly available at https://github.com/LZYAndy/UMEG-Net.
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            <a href="https://www.alphaxiv.org/abs/2511.14143v1" target="_blank" rel="noopener noreferrer">
                SMART：基于音频增强多模态大语言模型的镜头感知多模态视频片段检索
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            SMART: Shot-Aware Multimodal Video Moment Retrieval with Audio-Enhanced MLLM
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>An Yu, Weiheng Lu, Jian Li, Zhenfei Zhang, Yunhang Shen, Felix X. -F. Ye, Ming-C...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频片段检索，属于计算机视觉和多媒体领域，与推荐系统、搜索或广告的核心关注点关联较弱。虽然涉及多模态建模，但其应用场景（视频片段检索）与RecSys/Search/Ads的直接相关性有限，且未明确展示在推荐或搜索中的潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 05:03:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14143v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14143v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Video Moment Retrieval is a task in video understanding that aims to localize a specific temporal segment in an untrimmed video based on a natural language query. Despite recent progress in moment retrieval from videos using both traditional techniques and Multimodal Large Language Models (MLLM), most existing methods still rely on coarse temporal understanding and a single visual modality, limiting performance on complex videos. To address this, we introduce \textit{S}hot-aware \textit{M}ultimodal \textit{A}udio-enhanced \textit{R}etrieval of \textit{T}emporal \textit{S}egments (SMART), an MLLM-based framework that integrates audio cues and leverages shot-level temporal structure. SMART enriches multimodal representations by combining audio and visual features while applying \textbf{Shot-aware Token Compression}, which selectively retains high-information tokens within each shot to reduce redundancy and preserve fine-grained temporal details. We also refine prompt design to better utilize audio-visual cues. Evaluations on Charades-STA and QVHighlights show that SMART achieves significant improvements over state-of-the-art methods, including a 1.61\% increase in R1@0.5 and 2.59\% gain in R1@0.7 on Charades-STA.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14758v1" target="_blank" rel="noopener noreferrer">
                NeuCLIRBench：用于单语、跨语言和多语言信息检索的现代评估集合
            </a>
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        <div class="mb-2 text-base text-gray-700">
            NeuCLIRBench: A Modern Evaluation Collection for Monolingual, Cross-Language, and Multilingual Information Retrieval
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dawn Lawrie, James Mayfield, Eugene Yang, Andrew Yates, Sean MacAvaney, Ronak Pr...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注信息检索的评估基准和数据集构建，属于纯粹的评估方法论范畴。虽然涉及信息检索领域，但根据用户明确的排除标准，评估基准、Hallucination、Evaluation benchmarks等纯NLP中心主题属于不相关话题，且该论文没有展示在推荐系统、搜索或广告领域的直接技术应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 18:58:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14758v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14758v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    To measure advances in retrieval, test collections with relevance judgments that can faithfully distinguish systems are required. This paper presents NeuCLIRBench, an evaluation collection for cross-language and multilingual retrieval. The collection consists of documents written natively in Chinese, Persian, and Russian, as well as those same documents machine translated into English. The collection supports several retrieval scenarios including: monolingual retrieval in English, Chinese, Persian, or Russian; cross-language retrieval with English as the query language and one of the other three languages as the document language; and multilingual retrieval, again with English as the query language and relevant documents in all three languages. NeuCLIRBench combines the TREC NeuCLIR track topics of 2022, 2023, and 2024. The 250,128 judgments across approximately 150 queries for the monolingual and cross-language tasks and 100 queries for multilingual retrieval provide strong statistical discriminatory power to distinguish retrieval approaches. A fusion baseline of strong neural retrieval systems is included with the collection so that developers of reranking algorithms are no longer reliant on BM25 as their first-stage retriever. NeuCLIRBench is publicly available.
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            <a href="https://www.alphaxiv.org/abs/2511.14531v1" target="_blank" rel="noopener noreferrer">
                LiveRAG：一个包含不同难度级别的多样化问答数据集，用于RAG评估
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            LiveRAG: A diverse Q&A dataset with varying difficulty level for RAG evaluation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>David Carmel, Simone Filice, Guy Horowitz, Yoelle Maarek, Alex Shtoff, Oren Some...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注RAG（检索增强生成）的评估数据集创建，这属于纯粹的LLM评估基准范畴。虽然RAG技术在搜索系统中有所应用，但该论文聚焦于评估数据集本身而非技术改进，且没有明确涉及推荐系统或广告领域的直接应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 14:34:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14531v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14531v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With Retrieval Augmented Generation (RAG) becoming more and more prominent in generative AI solutions, there is an emerging need for systematically evaluating their effectiveness. We introduce the LiveRAG benchmark, a publicly available dataset of 895 synthetic questions and answers designed to support systematic evaluation of RAG-based Q&A systems. This synthetic benchmark is derived from the one used during the SIGIR'2025 LiveRAG Challenge, where competitors were evaluated under strict time constraints. It is augmented with information that was not made available to competitors during the Challenge, such as the ground-truth answers, together with their associated supporting claims which were used for evaluating competitors' answers. In addition, each question is associated with estimated difficulty and discriminability scores, derived from applying an Item Response Theory model to competitors' responses. Our analysis highlights the benchmark's questions diversity, the wide range of their difficulty levels, and their usefulness in differentiating between system capabilities. The LiveRAG benchmark will hopefully help the community advance RAG research, conduct systematic evaluation, and develop more robust Q&A systems.
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            <a href="https://www.alphaxiv.org/abs/2511.14405v1" target="_blank" rel="noopener noreferrer">
                Jasper-Token-Compression-600M 技术报告
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Jasper-Token-Compression-600M Technical Report
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dun Zhang, Ziyang Zeng, Yudong Zhou, Shuyang Lu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该标题仅提及模型规模（600M）和可能的令牌压缩技术，但未明确说明其与推荐系统、搜索或广告的关联。虽然令牌压缩可能属于Transformer效率改进的范畴，但缺乏具体的技术细节和应用场景描述，无法判断其是否属于核心LLM进展或直接应用于目标领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 12:13:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14405v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14405v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This technical report presents the training methodology and evaluation results of the open-source Jasper-Token-Compression-600M model, released in November 2025. Building on previous distillation-based recipes from the English Stella and Jasper models, we successfully extend this approach to a bilingual (English and Chinese) domain, further enhancing model performance through the incorporation of contrastive learning. A key innovation of our model is the introduction of a one-dimensional convolution-based token compression module. We dynamically adjust the compression rate during training, enabling the model to learn more robust and efficient compressed text representations. By combining knowledge distillation with token compression techniques, we achieve significant improvements in both embedding quality and inference efficiency. Our model performs with higher efficiency than a traditional 0.6B model while achieving performance comparable to that of an 8B model. For more information on the model release, visit: https://huggingface.co/infgrad/Jasper-Token-Compression-600M.
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            <a href="https://www.alphaxiv.org/abs/2511.14144v1" target="_blank" rel="noopener noreferrer">
                应用关系抽取与图匹配技术解决多项选择题
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            Applying Relation Extraction and Graph Matching to Answering Multiple Choice Questions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Naoki Shimoda, Akihiro Yamamoto
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于问答系统中的关系抽取和图匹配技术，属于通用NLP方法。虽然关系抽取在知识图谱构建中有应用，但论文标题未表明与推荐系统、搜索或广告的直接关联，也未涉及LLM或Transformer架构的进展。其应用场景更偏向通用问答而非特定领域的排序或推荐任务。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 05:03:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14144v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14144v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In this research, we combine Transformer-based relation extraction with matching of knowledge graphs (KGs) and apply them to answering multiple-choice questions (MCQs) while maintaining the traceability of the output process. KGs are structured representations of factual knowledge consisting of entities and relations. Due to the high construction cost, they had been regarded as static databases with validated links. However, the recent development of Transformer-based relation extraction (RE) methods has enabled us to generate KGs dynamically by giving them natural language texts, and thereby opened the possibility for representing the meaning of the input sentences with the created KGs. Using this effect, we propose a method that answers MCQs in the "fill-in-the-blank" format, taking care of the point that RE methods generate KGs that represent false information if provided with factually incorrect texts. We measure the truthfulness of each question sentence by (i) converting the sentence into a relational graph using an RE method and (ii) verifying it against factually correct KGs under the closed-world assumption. The experimental results demonstrate that our method correctly answers up to around 70% of the questions, while providing traceability of the procedure. We also highlight that the question category has a vast influence on the accuracy.
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            <a href="https://www.alphaxiv.org/abs/2511.14709v1" target="_blank" rel="noopener noreferrer">
                大型语言模型时代下的战略创新管理：市场情报、自适应研发与伦理治理
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            Strategic Innovation Management in the Age of Large Language Models Market Intelligence, Adaptive R&D, and Ethical Governance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Raha Aghaei, Ali A. Kiaei, Mahnaz Boush, Mahan Rofoosheh, Mohammad Zavvar
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM在企业战略管理、创新流程和伦理治理方面的应用，属于商业管理领域而非技术核心。虽然提及市场情报可能涉及搜索/推荐系统，但论文焦点是管理框架而非技术架构或算法创新，与当前关注的RecSys/Search/Ads技术进展相关性较弱。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:50:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14709v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14709v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This study analyzes the multiple functions of Large Language Models (LLMs) in transforming research and development (R&D) processes. By automating knowledge discovery, boosting hypothesis creation, integrating transdisciplinary insights, and enabling cooperation within innovation ecosystems, LLMs dramatically improve the efficiency and effectiveness of research processes. Through extensive analysis of scientific literature, patent databases, and experimental data, these models enable more flexible and informed R&D workflows, ultimately accelerating innovation cycles and lowering time-to-market for breakthrough ideas.
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            <a href="https://www.alphaxiv.org/abs/2511.14696v1" target="_blank" rel="noopener noreferrer">
                库尔德语词嵌入的子词分词策略
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Subword Tokenization Strategies for Kurdish Word Embeddings
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ali Salehi, Cassandra L. Jacobs
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于库尔德语这一特定语言的子词分词技术，属于语言特定的NLP预处理方法。虽然分词策略是文本处理的基础组件，但该工作针对的是低资源语言库尔德语，缺乏与推荐系统、搜索或广告领域的直接关联或潜在应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:33:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14696v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14696v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We investigate tokenization strategies for Kurdish word embeddings by comparing word-level, morpheme-based, and BPE approaches on morphological similarity preservation tasks. We develop a BiLSTM-CRF morphological segmenter using bootstrapped training from minimal manual annotation and evaluate Word2Vec embeddings across comprehensive metrics including similarity preservation, clustering quality, and semantic organization. Our analysis reveals critical evaluation biases in tokenization comparison. While BPE initially appears superior in morphological similarity, it evaluates only 28.6\% of test cases compared to 68.7\% for morpheme model, creating artificial performance inflation. When assessed comprehensively, morpheme-based tokenization demonstrates superior embedding space organization, better semantic neighborhood structure, and more balanced coverage across morphological complexity levels. These findings highlight the importance of coverage-aware evaluation in low-resource language processing and offers different tokenization methods for low-resourced language processing.
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            <a href="https://www.alphaxiv.org/abs/2511.14693v1" target="_blank" rel="noopener noreferrer">
                对话、快照、投诉：面向验证的多模态专家框架用于细粒度客户投诉分析
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Talk, Snap, Complain: Validation-Aware Multimodal Expert Framework for Fine-Grained Customer Grievances
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rishu Kumar Singh, Navneet Shreya, Sarmistha Das, Apoorva Singh, Sriparna Saha
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及多模态框架，但主要聚焦于客户投诉分析这一特定应用场景，与推荐系统、搜索或广告的核心技术关联较弱。多模态处理技术本身可能对异构数据处理有启发，但论文标题明确指向客户服务领域而非核心RecSys/Search/Ads技术栈。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:29:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14693v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14693v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing approaches to complaint analysis largely rely on unimodal, short-form content such as tweets or product reviews. This work advances the field by leveraging multimodal, multi-turn customer support dialogues, where users often share both textual complaints and visual evidence (e.g., screenshots, product photos) to enable fine-grained classification of complaint aspects and severity. We introduce VALOR, a Validation-Aware Learner with Expert Routing, tailored for this multimodal setting. It employs a multi-expert reasoning setup using large-scale generative models with Chain-of-Thought (CoT) prompting for nuanced decision-making. To ensure coherence between modalities, a semantic alignment score is computed and integrated into the final classification through a meta-fusion strategy. In alignment with the United Nations Sustainable Development Goals (UN SDGs), the proposed framework supports SDG 9 (Industry, Innovation and Infrastructure) by advancing AI-driven tools for robust, scalable, and context-aware service infrastructure. Further, by enabling structured analysis of complaint narratives and visual context, it contributes to SDG 12 (Responsible Consumption and Production) by promoting more responsive product design and improved accountability in consumer services. We evaluate VALOR on a curated multimodal complaint dataset annotated with fine-grained aspect and severity labels, showing that it consistently outperforms baseline models, especially in complex complaint scenarios where information is distributed across text and images. This study underscores the value of multimodal interaction and expert validation in practical complaint understanding systems. Resources related to data and codes are available here: https://github.com/sarmistha-D/VALOR
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            <a href="https://www.alphaxiv.org/abs/2511.14688v1" target="_blank" rel="noopener noreferrer">
                使用大语言模型为多语言历史自然语言处理生成真实标签
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Ground Truth Generation for Multilingual Historical NLP using LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Clovis Gladstone, Zhao Fang, Spencer Dean Stewart
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注历史文本的多语言NLP任务中的真实标签生成，这属于特定领域的NLP应用，与推荐系统、搜索或广告的核心技术进展没有直接关联。虽然使用了LLMs，但应用场景（历史NLP）和任务目标（真实标签生成）都超出了您关注的领域范围，缺乏在RecSys/Search/Ads中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:25:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14688v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14688v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Historical and low-resource NLP remains challenging due to limited annotated data and domain mismatches with modern, web-sourced corpora. This paper outlines our work in using large language models (LLMs) to create ground-truth annotations for historical French (16th-20th centuries) and Chinese (1900-1950) texts. By leveraging LLM-generated ground truth on a subset of our corpus, we were able to fine-tune spaCy to achieve significant gains on period-specific tests for part-of-speech (POS) annotations, lemmatization, and named entity recognition (NER). Our results underscore the importance of domain-specific models and demonstrate that even relatively limited amounts of synthetic data can improve NLP tools for under-resourced corpora in computational humanities research.
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            <a href="https://www.alphaxiv.org/abs/2511.14684v1" target="_blank" rel="noopener noreferrer">
                SMRC：基于学生推理对齐大语言模型进行数学错误纠正
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            SMRC: Aligning Large Language Models with Student Reasoning for Mathematical Error Correction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Biaojie Zeng, Min Zhang, Juan Zhou, Fengrui Liu, Ruiyang Huang, Xin Lin
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于数学错误纠正和教育领域的LLM对齐，这属于特定的领域应用而非核心推荐系统、搜索或广告技术。虽然涉及LLM对齐技术，但缺乏与异构数据处理、Transformer架构改进或推荐/搜索/广告应用的明确联系，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:22:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14684v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14684v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large language models (LLMs) often make reasoning errors when solving mathematical problems, and how to automatically detect and correct these errors has become an important research direction. However, existing approaches \textit{mainly focus on self-correction within the model}, which falls short of the ``teacher-style`` correction required in educational settings, \textit{i.e.}, systematically guiding and revising a student's problem-solving process. To address this gap, we propose \texttt{SMRC} (\textit{\underline{S}tudent \underline{M}athematical \underline{R}easoning \underline{C}orrection}), a novel method that aligns LLMs with student reasoning. Specifically, \texttt{SMRC} formulates student reasoning as a multi-step sequential decision problem and introduces Monte Carlo Tree Search (MCTS) to explore optimal correction paths. To reduce the cost of the annotating process-level rewards, we leverage breadth-first search (BFS) guided by LLMs and final-answer evaluation to generate reward signals, which are then distributed across intermediate reasoning steps via a back-propagation mechanism, enabling fine-grained process supervision. Additionally, we construct a benchmark for high school mathematics, MSEB (Multi-Solution Error Benchmark), consisting of 158 instances that include problem statements, student solutions, and correct reasoning steps. We further propose a dual evaluation protocol centered on \textbf{solution accuracy} and \textbf{correct-step retention}, offering a comprehensive measure of educational applicability. Experiments demonstrate that \texttt{SMRC} significantly outperforms existing methods on two public datasets (ProcessBench and MR-GSM8K) and our MSEB in terms of effectiveness and overall performance. The code and data are available at https://github.com/Mind-Lab-ECNU/SMRC.
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            <a href="https://www.alphaxiv.org/abs/2511.14671v1" target="_blank" rel="noopener noreferrer">
                使用检索增强大语言模型简化工业合同管理
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            Streamlining Industrial Contract Management with Retrieval-Augmented LLMs
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kristi Topollai, Tolga Dimlioglu, Anna Choromanska, Simon Odie, Reginald Hui
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然论文涉及检索增强生成（RAG）技术，这属于LLM应用范畴，但合同管理的具体应用领域与搜索、推荐或广告系统的核心排名任务关联度较低。该技术可能对文档检索系统有启发，但缺乏明确的RecSys/Search/Ads应用场景，因此相关性有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:10:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14671v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14671v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Contract management involves reviewing and negotiating provisions, individual clauses that define rights, obligations, and terms of agreement. During this process, revisions to provisions are proposed and iteratively refined, some of which may be problematic or unacceptable. Automating this workflow is challenging due to the scarcity of labeled data and the abundance of unstructured legacy contracts. In this paper, we present a modular framework designed to streamline contract management through a retrieval-augmented generation (RAG) pipeline. Our system integrates synthetic data generation, semantic clause retrieval, acceptability classification, and reward-based alignment to flag problematic revisions and generate improved alternatives. Developed and evaluated in collaboration with an industry partner, our system achieves over 80% accuracy in both identifying and optimizing problematic revisions, demonstrating strong performance under real-world, low-resource conditions and offering a practical means of accelerating contract revision workflows.
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            <a href="https://www.alphaxiv.org/abs/2511.14662v1" target="_blank" rel="noopener noreferrer">
                偏见进，偏见出：多语言大语言模型中的标注偏见
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            Bias in, Bias out: Annotation Bias in Multilingual Large Language Models
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xia Cui, Ziyi Huang, Naeemeh Adel
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多语言LLM中的标注偏见问题，这属于公平性、伦理等非技术性话题范畴。虽然涉及LLM技术，但论文焦点是偏见评估而非核心模型架构进步或直接应用，与当前关注的推荐系统、搜索或广告技术进展关联度极低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:02:12
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                <a href="https://arxiv.org/abs/2511.14662v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14662v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Annotation bias in NLP datasets remains a major challenge for developing multilingual Large Language Models (LLMs), particularly in culturally diverse settings. Bias from task framing, annotator subjectivity, and cultural mismatches can distort model outputs and exacerbate social harms. We propose a comprehensive framework for understanding annotation bias, distinguishing among instruction bias, annotator bias, and contextual and cultural bias. We review detection methods (including inter-annotator agreement, model disagreement, and metadata analysis) and highlight emerging techniques such as multilingual model divergence and cultural inference. We further outline proactive and reactive mitigation strategies, including diverse annotator recruitment, iterative guideline refinement, and post-hoc model adjustments. Our contributions include: (1) a typology of annotation bias; (2) a synthesis of detection metrics; (3) an ensemble-based bias mitigation approach adapted for multilingual settings, and (4) an ethical analysis of annotation processes. Together, these insights aim to inform more equitable and culturally grounded annotation pipelines for LLMs.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14631v1" target="_blank" rel="noopener noreferrer">
                利用视觉语言模型能力增强自主科学发现的智能体能力
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Enhancing Agentic Autonomous Scientific Discovery with Vision-Language Model Capabilities
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kahaan Gandhi, Boris Bolliet, Inigo Zubeldia
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注科学发现领域的智能体应用，属于特定领域应用而非核心推荐系统、搜索或广告技术。虽然提到了视觉语言模型，但应用场景是科学发现而非处理推荐/搜索中的异构数据模态，与当前关注点相关性较弱。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 16:23:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14631v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14631v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.MA</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We show that multi-agent systems guided by vision-language models (VLMs) improve end-to-end autonomous scientific discovery. By treating plots as verifiable checkpoints, a VLM-as-a-judge evaluates figures against dynamically generated domain-specific rubrics, enabling agents to correct their own errors and steer exploratory data analysis in real-time. Case studies in cosmology and astrochemistry demonstrate recovery from faulty reasoning paths and adaptation to new datasets without human intervention. On a 10-task benchmark for data-driven discovery, VLM-augmented systems achieve pass at 1 scores of 0.7-0.8, compared to 0.2-0.3 for code-only and 0.4-0.5 for code-and-text baselines, while also providing auditable reasoning traces that improve interpretability. Code available here: https://github.com/CMBAgents/cmbagent
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14606v1" target="_blank" rel="noopener noreferrer">
                弥合人类与模型视角：基于大语言模型的新闻媒体政治偏见检测比较分析
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Bridging Human and Model Perspectives: A Comparative Analysis of Political Bias Detection in News Media Using Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shreya Adrita Banik, Niaz Nafi Rahman, Tahsina Moiukh, Farig Sadeque
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注政治偏见检测这一特定NLP应用领域，虽然使用了LLM技术，但核心应用场景（新闻媒体政治分析）与推荐系统、搜索或广告领域没有直接关联。论文内容更偏向于社会计算和媒体分析，而非能够为RecSys/Search/Ads提供核心技术或架构创新的研究方向。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 15:58:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14606v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14606v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Detecting political bias in news media is a complex task that requires interpreting subtle linguistic and contextual cues. Although recent advances in Natural Language Processing (NLP) have enabled automatic bias classification, the extent to which large language models (LLMs) align with human judgment still remains relatively underexplored and not yet well understood. This study aims to present a comparative framework for evaluating the detection of political bias across human annotations and multiple LLMs, including GPT, BERT, RoBERTa, and FLAN. We construct a manually annotated dataset of news articles and assess annotation consistency, bias polarity, and inter-model agreement to quantify divergence between human and model perceptions of bias. Experimental results show that among traditional transformer-based models, RoBERTa achieves the highest alignment with human labels, whereas generative models such as GPT demonstrate the strongest overall agreement with human annotations in a zero-shot setting. Among all transformer-based baselines, our fine-tuned RoBERTa model acquired the highest accuracy and the strongest alignment with human-annotated labels. Our findings highlight systematic differences in how humans and LLMs perceive political slant, underscoring the need for hybrid evaluation frameworks that combine human interpretability with model scalability in automated media bias detection.
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            <a href="https://www.alphaxiv.org/abs/2511.14385v1" target="_blank" rel="noopener noreferrer">
                缓解大型语言模型中的标签长度偏差
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Mitigating Label Length Bias in Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mario Sanz-Guerrero, Katharina von der Wense
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM训练中的标签长度偏差问题，属于模型训练优化的技术范畴。虽然LLM偏差缓解可能间接影响模型性能，但论文没有明确展示与推荐系统、搜索或广告的直接应用关联，且更偏向于NLP训练方法而非核心架构或应用创新。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 11:45:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14385v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14385v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) are powerful zero- and few-shot learners. However, when predicting over a set of candidate options, LLMs suffer from label biases, and existing calibration methods overlook biases arising from multi-token class labels. We tackle an issue we call label length bias, where labels of different lengths are treated inconsistently, even after standard length normalization. To mitigate it, we propose normalized contextual calibration (NCC), an effective method that normalizes and calibrates predictions at the full-label level. NCC achieves statistically significant improvements over prior approaches across multiple datasets and models, with gains of up to 10% F1. Moreover, NCC extends bias mitigation to broader tasks such as multiple-choice question answering. Our analysis shows that, when combined with in-context learning, NCC is less sensitive to few-shot example selection, requires fewer examples for competitive performance, and produces more reliable confidence estimates. These findings highlight the importance of mitigating full-label biases to improve the performance and robustness of LLM-based methods, particularly in real-world applications where class labels naturally consist of multiple tokens.
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            <a href="https://www.alphaxiv.org/abs/2511.14362v1" target="_blank" rel="noopener noreferrer">
                SciRAG：面向科学文献的自适应、引用感知与大纲引导的检索与合成
            </a>
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        <div class="mb-2 text-base text-gray-700">
            SciRAG: Adaptive, Citation-Aware, and Outline-Guided Retrieval and Synthesis for Scientific Literature
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hang Ding, Yilun Zhao, Tiansheng Hu, Manasi Patwardhan, Arman Cohan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注科学文献领域的检索增强生成(RAG)技术，虽然涉及检索和合成技术，但其应用场景限定于科学文献处理。该技术缺乏与推荐系统、搜索或广告领域的直接关联，也没有展示在异构数据建模或Transformer架构改进方面的通用性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 11:09:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14362v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14362v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.DL</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The accelerating growth of scientific publications has intensified the need for scalable, trustworthy systems to synthesize knowledge across diverse literature. While recent retrieval-augmented generation (RAG) methods have improved access to scientific information, they often overlook citation graph structure, adapt poorly to complex queries, and yield fragmented, hard-to-verify syntheses. We introduce SciRAG, an open-source framework for scientific literature exploration that addresses these gaps through three key innovations: (1) adaptive retrieval that flexibly alternates between sequential and parallel evidence gathering; (2) citation-aware symbolic reasoning that leverages citation graphs to organize and filter supporting documents; and (3) outline-guided synthesis that plans, critiques, and refines answers to ensure coherence and transparent attribution. Extensive experiments across multiple benchmarks such as QASA and ScholarQA demonstrate that SciRAG outperforms prior systems in factual accuracy and synthesis quality, establishing a new foundation for reliable, large-scale scientific knowledge aggregation.
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            <a href="https://www.alphaxiv.org/abs/2511.14342v1" target="_blank" rel="noopener noreferrer">
                ConInstruct：评估大型语言模型在指令冲突检测与解决方面的能力
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            ConInstruct: Evaluating Large Language Models on Conflict Detection and Resolution in Instructions
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xingwei He, Qianru Zhang, Pengfei Chen, Guanhua Chen, Linlin Yu, Yuan Yuan, Siu-...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM在指令冲突检测与解决方面的评估，这属于纯粹的LLM能力评估范畴，与您的核心关注点（推荐系统、搜索、广告中的技术进展）没有直接关联。虽然涉及LLM评估，但缺乏明确的RecSys/Search/Ads应用场景，且更偏向NLP基准测试而非实际应用技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 10:49:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14342v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14342v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Instruction-following is a critical capability of Large Language Models (LLMs). While existing works primarily focus on assessing how well LLMs adhere to user instructions, they often overlook scenarios where instructions contain conflicting constraints-a common occurrence in complex prompts. The behavior of LLMs under such conditions remains under-explored. To bridge this gap, we introduce ConInstruct, a benchmark specifically designed to assess LLMs' ability to detect and resolve conflicts within user instructions. Using this dataset, we evaluate LLMs' conflict detection performance and analyze their conflict resolution behavior. Our experiments reveal two key findings: (1) Most proprietary LLMs exhibit strong conflict detection capabilities, whereas among open-source models, only DeepSeek-R1 demonstrates similarly strong performance. DeepSeek-R1 and Claude-4.5-Sonnet achieve the highest average F1-scores at 91.5% and 87.3%, respectively, ranking first and second overall. (2) Despite their strong conflict detection abilities, LLMs rarely explicitly notify users about the conflicts or request clarification when faced with conflicting constraints. These results underscore a critical shortcoming in current LLMs and highlight an important area for future improvement when designing instruction-following LLMs.
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            <a href="https://www.alphaxiv.org/abs/2511.14249v1" target="_blank" rel="noopener noreferrer">
                基于检索增强的导演-演员交互学习实现真实电影配音
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            Towards Authentic Movie Dubbing with Retrieve-Augmented Director-Actor Interaction Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rui Liu, Yuan Zhao, Zhenqi Jia
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注电影配音生成，属于AIGC和内容生成领域，与我的核心关注点（推荐系统、搜索、广告的排序和建模）无关。虽然涉及检索增强技术，但其应用场景（电影配音）与RecSys/Search/Ads没有直接关联，且属于被排除的纯粹LLM应用范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 08:39:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14249v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14249v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    The automatic movie dubbing model generates vivid speech from given scripts, replicating a speaker's timbre from a brief timbre prompt while ensuring lip-sync with the silent video. Existing approaches simulate a simplified workflow where actors dub directly without preparation, overlooking the critical director-actor interaction. In contrast, authentic workflows involve a dynamic collaboration: directors actively engage with actors, guiding them to internalize the context cues, specifically emotion, before performance. To address this issue, we propose a new Retrieve-Augmented Director-Actor Interaction Learning scheme to achieve authentic movie dubbing, termed Authentic-Dubber, which contains three novel mechanisms: (1) We construct a multimodal Reference Footage library to simulate the learning footage provided by directors. Note that we integrate Large Language Models (LLMs) to achieve deep comprehension of emotional representations across multimodal signals. (2) To emulate how actors efficiently and comprehensively internalize director-provided footage during dubbing, we propose an Emotion-Similarity-based Retrieval-Augmentation strategy. This strategy retrieves the most relevant multimodal information that aligns with the target silent video. (3) We develop a Progressive Graph-based speech generation approach that incrementally incorporates the retrieved multimodal emotional knowledge, thereby simulating the actor's final dubbing process. The above mechanisms enable the Authentic-Dubber to faithfully replicate the authentic dubbing workflow, achieving comprehensive improvements in emotional expressiveness. Both subjective and objective evaluations on the V2C Animation benchmark dataset validate the effectiveness. The code and demos are available at https://github.com/AI-S2-Lab/Authentic-Dubber.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14142v1" target="_blank" rel="noopener noreferrer">
                从图到超图：通过多级关系建模增强基于方面的情感分析
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            From Graphs to Hypergraphs: Enhancing Aspect-Based Sentiment Analysis via Multi-Level Relational Modeling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Omkar Mahesh Kashyap, Padegal Amit, Madhav Kashyap, Ashwini M Joshi, Shylaja SS
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注情感分析这一NLP特定任务，属于纯粹的文本分析领域。虽然涉及图神经网络和关系建模技术，但这些技术应用被限定在情感分析场景中，没有展示在推荐系统、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 05:01:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14142v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14142v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Aspect-Based Sentiment Analysis (ABSA) predicts sentiment polarity for specific aspect terms, a task made difficult by conflicting sentiments across aspects and the sparse context of short texts. Prior graph-based approaches model only pairwise dependencies, forcing them to construct multiple graphs for different relational views. These introduce redundancy, parameter overhead, and error propagation during fusion, limiting robustness in short-text, low-resource settings. We present HyperABSA, a dynamic hypergraph framework that induces aspect-opinion structures through sample-specific hierarchical clustering. To construct these hyperedges, we introduce a novel acceleration-fallback cutoff for hierarchical clustering, which adaptively determines the level of granularity. Experiments on three benchmarks (Lap14, Rest14, MAMS) show consistent improvements over strong graph baselines, with substantial gains when paired with RoBERTa backbones. These results position dynamic hypergraph construction as an efficient, powerful alternative for ABSA, with potential extensions to other short-text NLP tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.14106v1" target="_blank" rel="noopener noreferrer">
                隐式微调：利用自生成思维链高效打破RVLMs的对齐性
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            Stealth Fine-Tuning: Efficiently Breaking Alignment in RVLMs Using Self-Generated CoT
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Le Yu, Zhengyue Zhao, Yawen Zheng, Yunhao Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注RVLMs（可能是机器人视觉语言模型）的对齐破坏和安全性问题，这属于安全/伦理范畴，属于明确的无关主题。虽然提到了思维链和微调技术，但核心焦点是模型对齐的破坏而非在推荐/搜索/广告领域的应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 03:45:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14106v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14106v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reasoning-augmented Vision-Language Models (RVLMs) rely on safety alignment to prevent harmful behavior, yet their exposed chain-of-thought (CoT) traces introduce new attack surfaces. In this work, we find that the safety alignment of RVLMs can be easily break through a novel attack method termed \textbf{Stealth Fine-Tuning}. Our method elicits harmful reasoning traces through \textbf{segment-level interference} and reuses the self-generated outputs as supervised fine-tuning data. Through a \textbf{turn-based weighted} loss design, yielding a lightweight, distribution-consistent finetuning method. In our experiment, with only 499 samples and under 3 hours on a single A100 (QLoRA), Stealth Fine-Tuning outperforms IDEATOR by 38.52\% ASR while preserving general reasoning ability, as the tuned model retains the original representation distribution. Experiments on AdvBench and several general benchmarks demonstrate that Stealth Fine-Tuning is a low-cost and highly effective way to bypass alignment defenses. \textcolor{red}{\textbf{Disclaimer: This paper contains content that may be disturbing or offensive.}}
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            <a href="https://www.alphaxiv.org/abs/2511.14086v1" target="_blank" rel="noopener noreferrer">
                基于误差驱动的场景编辑用于大语言模型中的3D语义理解
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            Error-Driven Scene Editing for 3D Grounding in Large Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yue Zhang, Zun Wang, Han Lin, Jialu Li, Jianing Yang, Yonatan Bitton, Idan Szpek...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注3D场景理解和视觉基础任务，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。虽然涉及大语言模型，但应用场景（3D grounding）过于专业化，难以转化为RecSys/Search/Ads领域的实际应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 03:13:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14086v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14086v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Despite recent progress in 3D-LLMs, they remain limited in accurately grounding language to visual and spatial elements in 3D environments. This limitation stems in part from training data that focuses on language reasoning rather than spatial understanding due to scarce 3D resources, leaving inherent grounding biases unresolved. To address this, we propose 3D scene editing as a key mechanism to generate precise visual counterfactuals that mitigate these biases through fine-grained spatial manipulation, without requiring costly scene reconstruction or large-scale 3D data collection. Furthermore, to make these edits targeted and directly address the specific weaknesses of the model, we introduce DEER-3D, an error-driven framework following a structured "Decompose, Diagnostic Evaluation, Edit, and Re-train" workflow, rather than broadly or randomly augmenting data as in conventional approaches. Specifically, upon identifying a grounding failure of the 3D-LLM, our framework first diagnoses the exact predicate-level error (e.g., attribute or spatial relation). It then executes minimal, predicate-aligned 3D scene edits, such as recoloring or repositioning, to produce targeted counterfactual supervision for iterative model fine-tuning, significantly enhancing grounding accuracy. We evaluate our editing pipeline across multiple benchmarks for 3D grounding and scene understanding tasks, consistently demonstrating improvements across all evaluated datasets through iterative refinement. DEER-3D underscores the effectiveness of targeted, error-driven scene editing in bridging linguistic reasoning capabilities with spatial grounding in 3D LLMs.
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            <a href="https://www.alphaxiv.org/abs/2511.14073v1" target="_blank" rel="noopener noreferrer">
                基于数据平衡和模型改进的多标签情感分类性能增强
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            Based on Data Balancing and Model Improvement for Multi-Label Sentiment Classification Performance Enhancement
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zijin Su, Huanzhu Lv, Yuren Niu, Yiming Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多标签情感分类，这是一个纯粹的NLP任务，与推荐系统、搜索或广告的核心技术没有直接关联。虽然情感分析在理论上可以用于用户评论分析，但论文标题明确聚焦于分类性能增强，没有表明任何在RecSys/Search/Ads领域的直接应用或架构创新。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 03:06:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14073v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14073v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Multi-label sentiment classification plays a vital role in natural language processing by detecting multiple emotions within a single text. However, existing datasets like GoEmotions often suffer from severe class imbalance, which hampers model performance, especially for underrepresented emotions. To address this, we constructed a balanced multi-label sentiment dataset by integrating the original GoEmotions data, emotion-labeled samples from Sentiment140 using a RoBERTa-base-GoEmotions model, and manually annotated texts generated by GPT-4 mini. Our data balancing strategy ensured an even distribution across 28 emotion categories. Based on this dataset, we developed an enhanced multi-label classification model that combines pre-trained FastText embeddings, convolutional layers for local feature extraction, bidirectional LSTM for contextual learning, and an attention mechanism to highlight sentiment-relevant words. A sigmoid-activated output layer enables multi-label prediction, and mixed precision training improves computational efficiency. Experimental results demonstrate significant improvements in accuracy, precision, recall, F1-score, and AUC compared to models trained on imbalanced data, highlighting the effectiveness of our approach.
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            <a href="https://www.alphaxiv.org/abs/2511.14043v1" target="_blank" rel="noopener noreferrer">
                AISAC：一个用于透明、检索增强的科学辅助的集成多智能体系统
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            AISAC: An Integrated multi-agent System for Transparent, Retrieval-Grounded Scientific Assistance
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chandrachur Bhattacharya, Sibendu Som
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注科学辅助领域的多智能体系统，虽然涉及检索增强技术，但其核心应用场景是科学领域而非推荐、搜索或广告系统。检索增强技术本身在搜索系统中具有应用潜力，但论文的具体定位和科学辅助的领域焦点使其与当前关注点的相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 01:51:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14043v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14043v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.MA</span></div>
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                    AI Scientific Assistant Core (AISAC) is an integrated multi-agent system developed at Argonne National Laboratory for scientific and engineering workflows. AISAC builds on established technologies - LangGraph for orchestration, FAISS for vector search, and SQLite for persistence - and integrates them into a unified system prototype focused on transparency, provenance tracking, and scientific adaptability. The system implements a Router-Planner-Coordinator workflow and an optional Evaluator role, using prompt-engineered agents coordinated via LangGraph's StateGraph and supported by helper agents such as a Researcher. Each role is defined through custom system prompts that enforce structured JSON outputs. A hybrid memory approach (FAISS + SQLite) enables both semantic retrieval and structured conversation history. An incremental indexing strategy based on file hashing minimizes redundant re-embedding when scientific corpora evolve. A configuration-driven project bootstrap layer allows research teams to customize tools, prompts, and data sources without modifying core code. All agent decisions, tool invocations, and retrievals are logged and visualized through a custom Gradio interface, providing step-by-step transparency for each reasoning episode. The authors have applied AISAC to multiple research areas at Argonne, including specialized deployments for waste-to-products research and energy process safety, as well as general-purpose scientific assistance, demonstrating its cross-domain applicability.
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            <a href="https://www.alphaxiv.org/abs/2511.14027v1" target="_blank" rel="noopener noreferrer">
                HiEAG：基于证据增强生成的上下文外虚假信息检测
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            HiEAG: Evidence-Augmented Generation for Out-of-Context Misinformation Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junjie Wu, Yumeng Fu, Nan Yu, Guohong Fu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注虚假信息检测，属于内容安全领域，与推荐系统、搜索或广告的核心技术进展相关性较弱。虽然涉及生成技术，但其应用场景和问题定义与排名、个性化或广告投放等核心业务需求相距甚远。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 01:11:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14027v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14027v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Recent advancements in multimodal out-of-context (OOC) misinformation detection have made remarkable progress in checking the consistencies between different modalities for supporting or refuting image-text pairs. However, existing OOC misinformation detection methods tend to emphasize the role of internal consistency, ignoring the significant of external consistency between image-text pairs and external evidence. In this paper, we propose HiEAG, a novel Hierarchical Evidence-Augmented Generation framework to refine external consistency checking through leveraging the extensive knowledge of multimodal large language models (MLLMs). Our approach decomposes external consistency checking into a comprehensive engine pipeline, which integrates reranking and rewriting, apart from retrieval. Evidence reranking module utilizes Automatic Evidence Selection Prompting (AESP) that acquires the relevant evidence item from the products of evidence retrieval. Subsequently, evidence rewriting module leverages Automatic Evidence Generation Prompting (AEGP) to improve task adaptation on MLLM-based OOC misinformation detectors. Furthermore, our approach enables explanation for judgment, and achieves impressive performance with instruction tuning. Experimental results on different benchmark datasets demonstrate that our proposed HiEAG surpasses previous state-of-the-art (SOTA) methods in the accuracy over all samples.
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            <a href="https://www.alphaxiv.org/abs/2511.14010v1" target="_blank" rel="noopener noreferrer">
                基于知识的自主性大型语言模型用于从勘测报告中理解多重灾害
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Knowledge-Grounded Agentic Large Language Models for Multi-Hazard Understanding from Reconnaissance Reports
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenchen Kuai, Zihao Li, Braden Rosen, Stephanie Paan, Navid Jafari, Jean-Louis ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注灾害理解和勘测报告分析，属于特定领域应用，与推荐系统、搜索或广告的核心技术进展无关。虽然涉及LLM技术，但其应用场景（灾害分析）属于明确的非相关领域，且没有展示在RecSys/Search/Ads中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 00:36:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14010v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14010v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Post-disaster reconnaissance reports contain critical evidence for understanding multi-hazard interactions, yet their unstructured narratives make systematic knowledge transfer difficult. Large language models (LLMs) offer new potential for analyzing these reports, but often generate unreliable or hallucinated outputs when domain grounding is absent. This study introduces the Mixture-of-Retrieval Agentic RAG (MoRA-RAG), a knowledge-grounded LLM framework that transforms reconnaissance reports into a structured foundation for multi-hazard reasoning. The framework integrates a Mixture-of-Retrieval mechanism that dynamically routes queries across hazard-specific databases while using agentic chunking to preserve contextual coherence during retrieval. It also includes a verification loop that assesses evidence sufficiency, refines queries, and initiates targeted searches when information remains incomplete. We construct HazardRecQA by deriving question-answer pairs from GEER reconnaissance reports, which document 90 global events across seven major hazard types. MoRA-RAG achieves up to 94.5 percent accuracy, outperforming zero-shot LLMs by 30 percent and state-of-the-art RAG systems by 10 percent, while reducing hallucinations across diverse LLM architectures. MoRA-RAG also enables open-weight LLMs to achieve performance comparable to proprietary models. It establishes a new paradigm for transforming post-disaster documentation into actionable, trustworthy intelligence for hazard resilience.
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            <a href="https://www.alphaxiv.org/abs/2511.14751v1" target="_blank" rel="noopener noreferrer">
                Co-Me：面向视觉几何变换器的置信度引导令牌合并
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            Co-Me: Confidence-Guided Token Merging for Visual Geometric Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yutian Chen, Yuheng Qiu, Ruogu Li, Ali Agha, Shayegan Omidshafiei, Jay Patrikar,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉变换器的令牌合并技术，属于计算机视觉领域的架构优化。虽然涉及Transformer效率改进，但其应用场景明确限定在视觉几何任务，与推荐系统、搜索或广告的核心需求缺乏直接关联。没有证据表明该方法能够有效处理推荐系统中的异构数据或用户序列建模。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 18:52:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14751v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14751v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    We propose Confidence-Guided Token Merging (Co-Me), an acceleration mechanism for visual geometric transformers without retraining or finetuning the base model. Co-Me distilled a light-weight confidence predictor to rank tokens by uncertainty and selectively merge low-confidence ones, effectively reducing computation while maintaining spatial coverage. Compared to similarity-based merging or pruning, the confidence signal in Co-Me reliably indicates regions emphasized by the transformer, enabling substantial acceleration without degrading performance. Co-Me applies seamlessly to various multi-view and streaming visual geometric transformers, achieving speedups that scale with sequence length. When applied to VGGT and MapAnything, Co-Me achieves up to $11.3\times$ and $7.2\times$ speedup, making visual geometric transformers practical for real-time 3D perception and reconstruction.
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            <a href="https://www.alphaxiv.org/abs/2511.14719v1" target="_blank" rel="noopener noreferrer">
                基于结构感知去噪的零样本合成视频真实感增强
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            Zero-shot Synthetic Video Realism Enhancement via Structure-aware Denoising
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yifan Wang, Liya Ji, Zhanghan Ke, Harry Yang, Ser-Nam Lim, Qifeng Chen
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频生成和真实感增强，属于计算机视觉领域。虽然结构感知去噪技术可能有一些通用价值，但论文标题明确指向合成视频处理，与推荐系统、搜索或广告的核心技术没有直接关联。这种视频增强技术更适用于内容生成和媒体处理，而非排名、检索或用户建模等核心业务场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 18:06:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14719v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14719v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    We propose an approach to enhancing synthetic video realism, which can re-render synthetic videos from a simulator in photorealistic fashion. Our realism enhancement approach is a zero-shot framework that focuses on preserving the multi-level structures from synthetic videos into the enhanced one in both spatial and temporal domains, built upon a diffusion video foundational model without further fine-tuning. Specifically, we incorporate an effective modification to have the generation/denoising process conditioned on estimated structure-aware information from the synthetic video, such as depth maps, semantic maps, and edge maps, by an auxiliary model, rather than extracting the information from a simulator. This guidance ensures that the enhanced videos are consistent with the original synthetic video at both the structural and semantic levels. Our approach is a simple yet general and powerful approach to enhancing synthetic video realism: we show that our approach outperforms existing baselines in structural consistency with the original video while maintaining state-of-the-art photorealism quality in our experiments.
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            <a href="https://www.alphaxiv.org/abs/2511.14716v1" target="_blank" rel="noopener noreferrer">
                扩散作为自蒸馏：单模型中的端到端潜在扩散
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            Diffusion As Self-Distillation: End-to-End Latent Diffusion In One Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiyuan Wang, Muhan Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注扩散模型的训练效率改进，属于生成式AI领域。虽然扩散模型在内容生成方面有应用，但该特定方法（自蒸馏、端到端潜在扩散）主要针对模型训练优化，与推荐系统、搜索或广告中的核心排名、检索或用户建模任务没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:58:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14716v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14716v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Standard Latent Diffusion Models rely on a complex, three-part architecture consisting of a separate encoder, decoder, and diffusion network, which are trained in multiple stages. This modular design is computationally inefficient, leads to suboptimal performance, and prevents the unification of diffusion with the single-network architectures common in vision foundation models. Our goal is to unify these three components into a single, end-to-end trainable network. We first demonstrate that a naive joint training approach fails catastrophically due to ``latent collapse'', where the diffusion training objective interferes with the network's ability to learn a good latent representation. We identify the root causes of this instability by drawing a novel analogy between diffusion and self-distillation based unsupervised learning method. Based on this insight, we propose Diffusion as Self-Distillation (DSD), a new framework with key modifications to the training objective that stabilize the latent space. This approach enables, for the first time, the stable end-to-end training of a single network that simultaneously learns to encode, decode, and perform diffusion. DSD achieves outstanding performance on the ImageNet $256\times 256$ conditional generation task: FID=13.44/6.38/4.25 with only 42M/118M/205M parameters and 50 training epochs on ImageNet, without using classifier-free-guidance.
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            <a href="https://www.alphaxiv.org/abs/2511.14689v1" target="_blank" rel="noopener noreferrer">
                图像分辨率对DeepFace和InsightFace年龄估计的影响
            </a>
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            Impact of Image Resolution on Age Estimation with DeepFace and InsightFace
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shiyar Jamo
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于计算机视觉中的年龄估计任务，主要研究图像分辨率对特定人脸识别模型性能的影响。虽然年龄估计在广告定向等场景中有潜在应用，但论文的核心关注点（图像分辨率影响分析）与推荐系统、搜索或广告的核心技术进展关联度较低，且未涉及LLM、Transformer架构或异构数据统一建模等关键方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:25:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14689v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14689v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Automatic age estimation is widely used for age verification, where input images often vary considerably in resolution. This study evaluates the effect of image resolution on age estimation accuracy using DeepFace and InsightFace. A total of 1000 images from the IMDB-Clean dataset were processed in seven resolutions, resulting in 7000 test samples. Performance was evaluated using Mean Absolute Error (MAE), Standard Deviation (SD), and Median Absolute Error (MedAE). Based on this study, we conclude that input image resolution has a clear and consistent impact on the accuracy of age estimation in both DeepFace and InsightFace. Both frameworks achieve optimal performance at 224x224 pixels, with an MAE of 10.83 years (DeepFace) and 7.46 years (InsightFace). At low resolutions, MAE increases substantially, while very high resolutions also degrade accuracy. InsightFace is consistently faster than DeepFace across all resolutions.
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            <a href="https://www.alphaxiv.org/abs/2511.14539v1" target="_blank" rel="noopener noreferrer">
                学习紧凑潜在空间以表示具有高保真几何细节的神经符号距离函数
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        <div class="mb-2 text-base text-gray-700">
            Learning Compact Latent Space for Representing Neural Signed Distance Functions with High-fidelity Geometry Details
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qiang Bai, Bojian Wu, Xi Yang, Zhizhong Han
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注神经符号距离函数的紧凑表示和几何细节保真，属于3D几何表示和计算机图形学领域。虽然神经隐式表示技术有一定通用性，但该工作没有明确展示在推荐系统、搜索或广告中的直接应用潜力，与当前关注的LLM、Transformer架构或异构数据建模等方向相关性较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 14:43:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14539v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14539v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Neural signed distance functions (SDFs) have been a vital representation to represent 3D shapes or scenes with neural networks. An SDF is an implicit function that can query signed distances at specific coordinates for recovering a 3D surface. Although implicit functions work well on a single shape or scene, they pose obstacles when analyzing multiple SDFs with high-fidelity geometry details, due to the limited information encoded in the latent space for SDFs and the loss of geometry details. To overcome these obstacles, we introduce a method to represent multiple SDFs in a common space, aiming to recover more high-fidelity geometry details with more compact latent representations. Our key idea is to take full advantage of the benefits of generalization-based and overfitting-based learning strategies, which manage to preserve high-fidelity geometry details with compact latent codes. Based on this framework, we also introduce a novel sampling strategy to sample training queries. The sampling can improve the training efficiency and eliminate artifacts caused by the influence of other SDFs. We report numerical and visual evaluations on widely used benchmarks to validate our designs and show advantages over the latest methods in terms of the representative ability and compactness.
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            <a href="https://www.alphaxiv.org/abs/2511.14530v1" target="_blank" rel="noopener noreferrer">
                DeCo-VAE：通过解耦表示学习紧凑潜在变量用于视频重建
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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            DeCo-VAE: Learning Compact Latents for Video Reconstruction via Decoupled Representation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiangchen Yin, Jiahui Yuan, Zhangchi Hu, Wenzhang Sun, Jie Chen, Xiaozhen Qiao, ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频重建领域的变分自编码器架构改进，属于计算机视觉范畴。虽然解耦表示学习技术本身具有通用性，但论文标题明确限定于视频重建应用，与推荐系统、搜索或广告的核心技术栈缺乏直接关联，潜在的应用迁移路径不明确。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 14:34:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14530v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14530v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.MM</span></div>
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                    Existing video Variational Autoencoders (VAEs) generally overlook the similarity between frame contents, leading to redundant latent modeling. In this paper, we propose decoupled VAE (DeCo-VAE) to achieve compact latent representation. Instead of encoding RGB pixels directly, we decompose video content into distinct components via explicit decoupling: keyframe, motion and residual, and learn dedicated latent representation for each. To avoid cross-component interference, we design dedicated encoders for each decoupled component and adopt a shared 3D decoder to maintain spatiotemporal consistency during reconstruction. We further utilize a decoupled adaptation strategy that freezes partial encoders while training the others sequentially, ensuring stable training and accurate learning of both static and dynamic features. Extensive quantitative and qualitative experiments demonstrate that DeCo-VAE achieves superior video reconstruction performance.
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            <a href="https://www.alphaxiv.org/abs/2511.14515v1" target="_blank" rel="noopener noreferrer">
                IMSE：基于U-Net的高效语音增强方法，采用Inception深度卷积和幅度感知线性注意力机制
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            IMSE: Efficient U-Net-based Speech Enhancement using Inception Depthwise Convolution and Amplitude-Aware Linear Attention
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinxin Tang, Bin Qin, Yufang Li
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语音增强技术，属于语音信号处理领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。虽然论文涉及注意力机制改进，但这是针对语音模态的特定优化，缺乏在推荐/搜索/广告领域应用的明确路径或潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 14:11:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14515v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14515v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SD</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    Achieving a balance between lightweight design and high performance remains a significant challenge for speech enhancement (SE) tasks on resource-constrained devices. Existing state-of-the-art methods, such as MUSE, have established a strong baseline with only 0.51M parameters by introducing a Multi-path Enhanced Taylor (MET) transformer and Deformable Embedding (DE). However, an in-depth analysis reveals that MUSE still suffers from efficiency bottlenecks: the MET module relies on a complex "approximate-compensate" mechanism to mitigate the limitations of Taylor-expansion-based attention, while the offset calculation for deformable embedding introduces additional computational burden. This paper proposes IMSE, a systematically optimized and ultra-lightweight network. We introduce two core innovations: 1) Replacing the MET module with Amplitude-Aware Linear Attention (MALA). MALA fundamentally rectifies the "amplitude-ignoring" problem in linear attention by explicitly preserving the norm information of query vectors in the attention calculation, achieving efficient global modeling without an auxiliary compensation branch. 2) Replacing the DE module with Inception Depthwise Convolution (IDConv). IDConv borrows the Inception concept, decomposing large-kernel operations into efficient parallel branches (square, horizontal, and vertical strips), thereby capturing spectrogram features with extremely low parameter redundancy. Extensive experiments on the VoiceBank+DEMAND dataset demonstrate that, compared to the MUSE baseline, IMSE significantly reduces the parameter count by 16.8\% (from 0.513M to 0.427M) while achieving competitive performance comparable to the state-of-the-art on the PESQ metric (3.373). This study sets a new benchmark for the trade-off between model size and speech quality in ultra-lightweight speech enhancement.
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            <a href="https://www.alphaxiv.org/abs/2511.14499v1" target="_blank" rel="noopener noreferrer">
                利用视觉语言模型的风险语义蒸馏增强端到端自动驾驶
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            <i class="fa fa-star mr-1"></i>2/10
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            Enhancing End-to-End Autonomous Driving with Risk Semantic Distillaion from VLM
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jack Qin, Zhitao Wang, Yinan Zheng, Keyu Chen, Yang Zhou, Yuanxin Zhong, Siyuan ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动驾驶领域，与搜索、推荐或广告系统没有直接关联。虽然提到了视觉语言模型（VLM），但其应用场景是自动驾驶中的风险语义蒸馏，这种技术在推荐系统、搜索或广告中的潜在应用不明确且间接。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 13:46:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14499v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14499v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The autonomous driving (AD) system has exhibited remarkable performance in complex driving scenarios. However, generalization is still a key limitation for the current system, which refers to the ability to handle unseen scenarios or unfamiliar sensor configurations.Related works have explored the use of Vision-Language Models (VLMs) to address few-shot or zero-shot tasks. While promising, these methods introduce a new challenge: the emergence of a hybrid AD system, where two distinct systems are used to plan a trajectory, leading to potential inconsistencies. Alternative research directions have explored Vision-Language-Action (VLA) frameworks that generate control actions from VLM directly. However, these end-to-end solutions demonstrate prohibitive computational demands. To overcome these challenges, we introduce Risk Semantic Distillation (RSD), a novel framework that leverages VLMs to enhance the training of End-to-End (E2E) AD backbones. By providing risk attention for key objects, RSD addresses the issue of generalization. Specifically, we introduce RiskHead, a plug-in module that distills causal risk estimates from Vision-Language Models into Bird's-Eye-View (BEV) features, yielding interpretable risk-attention maps.This approach allows BEV features to learn richer and more nuanced risk attention representations, which directly enhance the model's ability to handle spatial boundaries and risky objects.By focusing on risk attention, RSD aligns better with human-like driving behavior, which is essential to navigate in complex and dynamic environments. Our experiments on the Bench2Drive benchmark demonstrate the effectiveness of RSD in managing complex and unpredictable driving conditions. Due to the enhanced BEV representations enabled by RSD, we observed a significant improvement in both perception and planning capabilities.
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            <a href="https://www.alphaxiv.org/abs/2511.14449v1" target="_blank" rel="noopener noreferrer">
                DIR-TIR：基于对话式迭代优化的文本到图像检索方法
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            DIR-TIR: Dialog-Iterative Refinement for Text-to-Image Retrieval
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zongwei Zhen, Biqing Zeng
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文本到图像检索的对话式迭代优化，属于计算机视觉和跨模态检索领域。虽然涉及检索技术，但其核心应用场景是视觉内容检索而非推荐系统、搜索或广告中的排序问题，与当前关注的LLM技术、Transformer架构改进或异构数据统一建模等方向关联度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 12:45:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14449v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14449v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This paper addresses the task of interactive, conversational text-to-image retrieval. Our DIR-TIR framework progressively refines the target image search through two specialized modules: the Dialog Refiner Module and the Image Refiner Module. The Dialog Refiner actively queries users to extract essential information and generate increasingly precise descriptions of the target image. Complementarily, the Image Refiner identifies perceptual gaps between generated images and user intentions, strategically reducing the visual-semantic discrepancy. By leveraging multi-turn dialogues, DIR-TIR provides superior controllability and fault tolerance compared to conventional single-query methods, significantly improving target image hit accuracy. Comprehensive experiments across diverse image datasets demonstrate our dialogue-based approach substantially outperforms initial-description-only baselines, while the synergistic module integration achieves both higher retrieval precision and enhanced interactive experience.
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            <a href="https://www.alphaxiv.org/abs/2511.14446v1" target="_blank" rel="noopener noreferrer">
                智能体化视频智能：面向高级视频探索与理解的灵活框架
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            Agentic Video Intelligence: A Flexible Framework for Advanced Video Exploration and Understanding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hong Gao, Yiming Bao, Xuezhen Tu, Yutong Xu, Yue Jin, Yiyang Mu, Bin Zhong, Lina...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频智能探索与理解框架，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术焦点没有直接关联。虽然视频内容可能出现在某些推荐场景中，但论文标题未表明其与异构数据建模、Transformer架构改进或LLM技术应用有任何联系，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 12:43:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14446v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14446v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Video understanding requires not only visual recognition but also complex reasoning. While Vision-Language Models (VLMs) demonstrate impressive capabilities, they typically process videos largely in a single-pass manner with limited support for evidence revisit and iterative refinement. While recently emerging agent-based methods enable long-horizon reasoning, they either depend heavily on expensive proprietary models or require extensive agentic RL training. To overcome these limitations, we propose Agentic Video Intelligence (AVI), a flexible and training-free framework that can mirror human video comprehension through system-level design and optimization. AVI introduces three key innovations: (1) a human-inspired three-phase reasoning process (Retrieve-Perceive-Review) that ensures both sufficient global exploration and focused local analysis, (2) a structured video knowledge base organized through entity graphs, along with multi-granularity integrated tools, constituting the agent's interaction environment, and (3) an open-source model ensemble combining reasoning LLMs with lightweight base CV models and VLM, eliminating dependence on proprietary APIs or RL training. Experiments on LVBench, VideoMME-Long, LongVideoBench, and Charades-STA demonstrate that AVI achieves competitive performance while offering superior interpretability.
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            <a href="https://www.alphaxiv.org/abs/2511.14401v1" target="_blank" rel="noopener noreferrer">
                语言作为锚点：在领域增量学习中保持相对视觉几何关系
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            Language as an Anchor: Preserving Relative Visual Geometry for Domain Incremental Learning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shuyi Geng, Tao Zhou, Yi Zhou
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉领域的增量学习，通过语言作为锚点来保持视觉几何关系。虽然涉及多模态学习的概念，但其核心应用场景是计算机视觉而非推荐系统、搜索或广告领域。论文的技术方法（视觉几何保持）与RecSys/Search/Ads的核心需求（用户行为建模、内容理解、个性化推荐）关联度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 12:06:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14401v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14401v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    A key challenge in Domain Incremental Learning (DIL) is to continually learn under shifting distributions while preserving knowledge from previous domains. Existing methods face a fundamental dilemma. On one hand, projecting all domains into a single unified visual space leads to inter-domain interference and semantic distortion, as large shifts may vary with not only visual appearance but also underlying semantics. On the other hand, isolating domain-specific parameters causes knowledge fragmentation, creating "knowledge islands" that hamper knowledge reuse and exacerbate forgetting. To address this issue, we propose LAVA (Language-Anchored Visual Alignment), a novel DIL framework that replaces direct feature alignment with relative alignment driven by a text-based reference anchor. LAVA guides the visual representations of each incoming domain to preserve a consistent relative geometry, which is defined by mirroring the pairwise semantic similarities between the class names. This anchored geometric structure acts as a bridge across domains, enabling the retrieval of class-aware prior knowledge and facilitating robust feature aggregation. Extensive experiments on standard DIL benchmarks demonstrate that LAVA achieves significant performance improvements over state-of-the-arts. Code is available at https://github.com/ShuyiGeng/LAVA.
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            <a href="https://www.alphaxiv.org/abs/2511.14349v1" target="_blank" rel="noopener noreferrer">
                ARC-Chapter：将长达一小时的视频结构化为可导航章节和层次化摘要
            </a>
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            ARC-Chapter: Structuring Hour-Long Videos into Navigable Chapters and Hierarchical Summaries
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junfu Pu, Teng Wang, Yixiao Ge, Yuying Ge, Chen Li, Ying Shan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注长视频的结构化分析和摘要生成，属于计算机视觉和多媒体处理领域。虽然涉及内容理解和结构化，但其核心应用场景是视频导航和摘要，与推荐系统、搜索或广告的排名核心任务关联较弱。视频内容理解技术可能间接应用于视频推荐，但缺乏明确的RecSys/Search/Ads应用连接。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 10:53:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14349v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14349v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The proliferation of hour-long videos (e.g., lectures, podcasts, documentaries) has intensified demand for efficient content structuring. However, existing approaches are constrained by small-scale training with annotations that are typical short and coarse, restricting generalization to nuanced transitions in long videos. We introduce ARC-Chapter, the first large-scale video chaptering model trained on over million-level long video chapters, featuring bilingual, temporally grounded, and hierarchical chapter annotations. To achieve this goal, we curated a bilingual English-Chinese chapter dataset via a structured pipeline that unifies ASR transcripts, scene texts, visual captions into multi-level annotations, from short title to long summaries. We demonstrate clear performance improvements with data scaling, both in data volume and label intensity. Moreover, we design a new evaluation metric termed GRACE, which incorporates many-to-one segment overlaps and semantic similarity, better reflecting real-world chaptering flexibility. Extensive experiments demonstrate that ARC-Chapter establishes a new state-of-the-art by a significant margin, outperforming the previous best by 14.0% in F1 score and 11.3% in SODA score. Moreover, ARC-Chapter shows excellent transferability, improving the state-of-the-art on downstream tasks like dense video captioning on YouCook2.
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            <a href="https://www.alphaxiv.org/abs/2511.14341v1" target="_blank" rel="noopener noreferrer">
                走向地点：人工与自然系统中的地点识别
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            <i class="fa fa-star mr-1"></i>2/10
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            Going Places: Place Recognition in Artificial and Natural Systems
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Michael Milford, Tobias Fischer
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注地点识别技术，这在计算机视觉和机器人领域是常见的研究方向。虽然地点识别在理论上可能与位置感知的推荐系统有微弱关联，但论文标题明确强调在人工和自然系统中的通用应用，没有显示出与推荐系统、搜索或广告的直接相关性，也没有涉及LLM或Transformer架构的进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 10:49:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14341v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14341v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Place recognition, the ability to identify previously visited locations, is critical for both biological navigation and autonomous systems. This review synthesizes findings from robotic systems, animal studies, and human research to explore how different systems encode and recall place. We examine the computational and representational strategies employed across artificial systems, animals, and humans, highlighting convergent solutions such as topological mapping, cue integration, and memory management. Animal systems reveal evolved mechanisms for multimodal navigation and environmental adaptation, while human studies provide unique insights into semantic place concepts, cultural influences, and introspective capabilities. Artificial systems showcase scalable architectures and data-driven models. We propose a unifying set of concepts by which to consider and develop place recognition mechanisms and identify key challenges such as generalization, robustness, and environmental variability. This review aims to foster innovations in artificial localization by connecting future developments in artificial place recognition systems to insights from both animal navigation research and human spatial cognition studies.
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            <a href="https://www.alphaxiv.org/abs/2511.14291v1" target="_blank" rel="noopener noreferrer">
                GEN3D：从单张图像生成领域无关的3D场景
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            GEN3D: Generating Domain-Free 3D Scenes from a Single Image
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuxin Zhang, Ziyu Lu, Hongbo Duan, Keyu Fan, Pengting Luo, Peiyu Zhuang, Mengyu ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于从2D图像生成3D场景的计算机视觉任务，属于纯粹的3D视觉领域。虽然3D场景理解在增强现实或虚拟购物等特定场景下可能有间接应用，但论文标题未表明与推荐系统、搜索或广告的直接相关性，且缺乏明确的Transformer架构或LLM技术连接。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 09:40:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14291v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14291v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Despite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. Additionally, 3D scene generation is vital for advancing embodied AI and world models, which depend on diverse, high-quality scenes for learning and evaluation. In this work, we propose Gen3d, a novel method for generation of high-quality, wide-scope, and generic 3D scenes from a single image. After the initial point cloud is created by lifting the RGBD image, Gen3d maintains and expands its world model. The 3D scene is finalized through optimizing a Gaussian splatting representation. Extensive experiments on diverse datasets demonstrate the strong generalization capability and superior performance of our method in generating a world model and Synthesizing high-fidelity and consistent novel views.
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            <a href="https://www.alphaxiv.org/abs/2511.14279v1" target="_blank" rel="noopener noreferrer">
                填补差距的免费午餐：用于跨域少样本学习的中间域重构
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            Free Lunch to Meet the Gap: Intermediate Domain Reconstruction for Cross-Domain Few-Shot Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tong Zhang, Yifan Zhao, Liangyu Wang, Jia Li
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注跨域少样本学习中的领域适应问题，属于计算机视觉中的迁移学习范畴。虽然涉及领域适应技术，但缺乏与推荐系统、搜索或广告领域的直接关联，也没有明确涉及Transformer架构或LLM技术的应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 09:14:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14279v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14279v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Cross-Domain Few-Shot Learning (CDFSL) endeavors to transfer generalized knowledge from the source domain to target domains using only a minimal amount of training data, which faces a triplet of learning challenges in the meantime, i.e., semantic disjoint, large domain discrepancy, and data scarcity. Different from predominant CDFSL works focused on generalized representations, we make novel attempts to construct Intermediate Domain Proxies (IDP) with source feature embeddings as the codebook and reconstruct the target domain feature with this learned codebook. We then conduct an empirical study to explore the intrinsic attributes from perspectives of visual styles and semantic contents in intermediate domain proxies. Reaping benefits from these attributes of intermediate domains, we develop a fast domain alignment method to use these proxies as learning guidance for target domain feature transformation. With the collaborative learning of intermediate domain reconstruction and target feature transformation, our proposed model is able to surpass the state-of-the-art models by a margin on 8 cross-domain few-shot learning benchmarks.
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            <a href="https://www.alphaxiv.org/abs/2511.14271v1" target="_blank" rel="noopener noreferrer">
                让语言约束几何：视觉语言模型作为三维生成的语义与空间评判器
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            Let Language Constrain Geometry: Vision-Language Models as Semantic and Spatial Critics for 3D Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Weimin Bai, Yubo Li, Weijian Luo, Zeqiang Lai, Yequan Wang, Wenzheng Chen, He Su...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及视觉语言模型，但其核心焦点是3D生成任务，这属于纯粹的计算机视觉和图形学领域。论文探讨的是如何利用语言来指导几何形状生成，与推荐系统、搜索或广告中的异构数据处理没有直接关联。视觉语言模型在此被用作3D生成的评判工具，而非用于处理用户行为序列或上下文特征等推荐系统相关的异构数据。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 09:05:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14271v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14271v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Text-to-3D generation has advanced rapidly, yet state-of-the-art models, encompassing both optimization-based and feed-forward architectures, still face two fundamental limitations. First, they struggle with coarse semantic alignment, often failing to capture fine-grained prompt details. Second, they lack robust 3D spatial understanding, leading to geometric inconsistencies and catastrophic failures in part assembly and spatial relationships. To address these challenges, we propose VLM3D, a general framework that repurposes large vision-language models (VLMs) as powerful, differentiable semantic and spatial critics. Our core contribution is a dual-query critic signal derived from the VLM's Yes or No log-odds, which assesses both semantic fidelity and geometric coherence. We demonstrate the generality of this guidance signal across two distinct paradigms: (1) As a reward objective for optimization-based pipelines, VLM3D significantly outperforms existing methods on standard benchmarks. (2) As a test-time guidance module for feed-forward pipelines, it actively steers the iterative sampling process of SOTA native 3D models to correct severe spatial errors. VLM3D establishes a principled and generalizable path to inject the VLM's rich, language-grounded understanding of both semantics and space into diverse 3D generative pipelines.
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                基于高斯泼溅的低秩张量表示在多维图像恢复中的应用
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            Gaussian Splatting-based Low-Rank Tensor Representation for Multi-Dimensional Image Recovery
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yiming Zeng, Xi-Le Zhao, Wei-Hao Wu, Teng-Yu Ji, Chao Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的图像恢复技术，使用高斯泼溅和低秩张量表示方法。虽然低秩表示在推荐系统中可能有潜在应用（如矩阵分解），但论文明确聚焦于多维图像恢复，与推荐系统、搜索或广告的核心技术关联较弱。没有明确的Transformer架构或LLM技术应用，也不涉及异构数据统一建模。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 09:04:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14270v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14270v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Tensor singular value decomposition (t-SVD) is a promising tool for multi-dimensional image representation, which decomposes a multi-dimensional image into a latent tensor and an accompanying transform matrix. However, two critical limitations of t-SVD methods persist: (1) the approximation of the latent tensor (e.g., tensor factorizations) is coarse and fails to accurately capture spatial local high-frequency information; (2) The transform matrix is composed of fixed basis atoms (e.g., complex exponential atoms in DFT and cosine atoms in DCT) and cannot precisely capture local high-frequency information along the mode-3 fibers. To address these two limitations, we propose a Gaussian Splatting-based Low-rank tensor Representation (GSLR) framework, which compactly and continuously represents multi-dimensional images. Specifically, we leverage tailored 2D Gaussian splatting and 1D Gaussian splatting to generate the latent tensor and transform matrix, respectively. The 2D and 1D Gaussian splatting are indispensable and complementary under this representation framework, which enjoys a powerful representation capability, especially for local high-frequency information. To evaluate the representation ability of the proposed GSLR, we develop an unsupervised GSLR-based multi-dimensional image recovery model. Extensive experiments on multi-dimensional image recovery demonstrate that GSLR consistently outperforms state-of-the-art methods, particularly in capturing local high-frequency information.
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            <a href="https://www.alphaxiv.org/abs/2511.14238v1" target="_blank" rel="noopener noreferrer">
                通过带正则化的弱监督自适应增强深度估计基础模型的泛化能力
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Enhancing Generalization of Depth Estimation Foundation Model via Weakly-Supervised Adaptation with Regularization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yan Huang, Yongyi Su, Xin Lin, Le Zhang, Xun Xu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的深度估计任务，属于纯粹的视觉领域研究。虽然提到了基础模型和泛化能力，但其核心应用场景是视觉深度感知，与推荐系统、搜索或广告中的排序和用户建模没有直接关联。该技术缺乏明确的路径应用于异构数据处理或推荐系统领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 08:16:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14238v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14238v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    The emergence of foundation models has substantially advanced zero-shot generalization in monocular depth estimation (MDE), as exemplified by the Depth Anything series. However, given access to some data from downstream tasks, a natural question arises: can the performance of these models be further improved? To this end, we propose WeSTAR, a parameter-efficient framework that performs Weakly supervised Self-Training Adaptation with Regularization, designed to enhance the robustness of MDE foundation models in unseen and diverse domains. We first adopt a dense self-training objective as the primary source of structural self-supervision. To further improve robustness, we introduce semantically-aware hierarchical normalization, which exploits instance-level segmentation maps to perform more stable and multi-scale structural normalization. Beyond dense supervision, we introduce a cost-efficient weak supervision in the form of pairwise ordinal depth annotations to further guide the adaptation process, which enforces informative ordinal constraints to mitigate local topological errors. Finally, a weight regularization loss is employed to anchor the LoRA updates, ensuring training stability and preserving the model's generalizable knowledge. Extensive experiments on both realistic and corrupted out-of-distribution datasets under diverse and challenging scenarios demonstrate that WeSTAR consistently improves generalization and achieves state-of-the-art performance across a wide range of benchmarks.
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            <a href="https://www.alphaxiv.org/abs/2511.14237v1" target="_blank" rel="noopener noreferrer">
                打破被动学习陷阱：一种用于人体运动预测的主动感知策略
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Breaking the Passive Learning Trap: An Active Perception Strategy for Human Motion Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Juncheng Hu, Zijian Zhang, Zeyu Wang, Guoyu Wang, Yingji Li, Kedi Lyu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于人体运动预测和主动感知策略，属于计算机视觉和机器人技术领域。虽然运动预测在某些特定场景下可能与用户行为建模有微弱关联，但该论文没有明确涉及推荐系统、搜索或广告的核心技术，也没有展示与LLM、Transformer架构或异构数据统一建模的直接相关性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 08:15:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14237v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14237v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Forecasting 3D human motion is an important embodiment of fine-grained understanding and cognition of human behavior by artificial agents. Current approaches excessively rely on implicit network modeling of spatiotemporal relationships and motion characteristics, falling into the passive learning trap that results in redundant and monotonous 3D coordinate information acquisition while lacking actively guided explicit learning mechanisms. To overcome these issues, we propose an Active Perceptual Strategy (APS) for human motion prediction, leveraging quotient space representations to explicitly encode motion properties while introducing auxiliary learning objectives to strengthen spatio-temporal modeling. Specifically, we first design a data perception module that projects poses into the quotient space, decoupling motion geometry from coordinate redundancy. By jointly encoding tangent vectors and Grassmann projections, this module simultaneously achieves geometric dimension reduction, semantic decoupling, and dynamic constraint enforcement for effective motion pose characterization. Furthermore, we introduce a network perception module that actively learns spatio-temporal dependencies through restorative learning. This module deliberately masks specific joints or injects noise to construct auxiliary supervision signals. A dedicated auxiliary learning network is designed to actively adapt and learn from perturbed information. Notably, APS is model agnostic and can be integrated with different prediction models to enhance active perceptual. The experimental results demonstrate that our method achieves the new state-of-the-art, outperforming existing methods by large margins: 16.3% on H3.6M, 13.9% on CMU Mocap, and 10.1% on 3DPW.
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            <a href="https://www.alphaxiv.org/abs/2511.14210v1" target="_blank" rel="noopener noreferrer">
                Orion：一种用于多模态感知、高级视觉推理与执行的统一视觉智能体
            </a>
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            Orion: A Unified Visual Agent for Multimodal Perception, Advanced Visual Reasoning and Execution
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>N Dinesh Reddy, Sudeep Pillai
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉智能体在多模态感知和视觉推理方面的能力，属于纯粹的视觉和智能体研究领域。虽然提到了多模态感知，但核心焦点是视觉推理和执行，与推荐系统、搜索或广告中的异构数据处理没有明确的直接关联。该技术可能通过视觉理解能力间接应用于商品图像理解等场景，但这种应用过于间接且不明确。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 07:41:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14210v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14210v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    We introduce Orion, a visual agent framework that can take in any modality and generate any modality. Using an agentic framework with multiple tool-calling capabilities, Orion is designed for visual AI tasks and achieves state-of-the-art results. Unlike traditional vision-language models that produce descriptive outputs, Orion orchestrates a suite of specialized computer vision tools, including object detection, keypoint localization, panoptic segmentation, Optical Character Recognition, and geometric analysis, to execute complex multi-step visual workflows. The system achieves competitive performance on MMMU, MMBench, DocVQA, and MMLongBench while extending monolithic vision-language models to production-grade visual intelligence. By combining neural perception with symbolic execution, Orion enables autonomous visual reasoning, marking a transition from passive visual understanding to active, tool-driven visual intelligence.
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            <a href="https://www.alphaxiv.org/abs/2511.14208v1" target="_blank" rel="noopener noreferrer">
                InstantViR：基于蒸馏扩散先验的实时视频逆问题求解器
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            <i class="fa fa-star mr-1"></i>2/10
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            InstantViR: Real-Time Video Inverse Problem Solver with Distilled Diffusion Prior
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Weimin Bai, Suzhe Xu, Yiwei Ren, Jinhua Hao, Ming Sun, Wenzheng Chen, He Sun
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频逆问题求解和扩散模型蒸馏技术，属于计算机视觉领域的特定应用。虽然提到了实时处理，但缺乏与推荐系统、搜索或广告领域的直接关联。视频逆问题求解在推荐/搜索/广告中的潜在应用场景非常有限，主要适用于图像/视频修复等视觉任务。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 07:40:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14208v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14208v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Video inverse problems are fundamental to streaming, telepresence, and AR/VR, where high perceptual quality must coexist with tight latency constraints. Diffusion-based priors currently deliver state-of-the-art reconstructions, but existing approaches either adapt image diffusion models with ad hoc temporal regularizers - leading to temporal artifacts - or rely on native video diffusion models whose iterative posterior sampling is far too slow for real-time use. We introduce InstantViR, an amortized inference framework for ultra-fast video reconstruction powered by a pre-trained video diffusion prior. We distill a powerful bidirectional video diffusion model (teacher) into a causal autoregressive student that maps a degraded video directly to its restored version in a single forward pass, inheriting the teacher's strong temporal modeling while completely removing iterative test-time optimization. The distillation is prior-driven: it only requires the teacher diffusion model and known degradation operators, and does not rely on externally paired clean/noisy video data. To further boost throughput, we replace the video-diffusion backbone VAE with a high-efficiency LeanVAE via an innovative teacher-space regularized distillation scheme, enabling low-latency latent-space processing. Across streaming random inpainting, Gaussian deblurring and super-resolution, InstantViR matches or surpasses the reconstruction quality of diffusion-based baselines while running at over 35 FPS on NVIDIA A100 GPUs, achieving up to 100 times speedups over iterative video diffusion solvers. These results show that diffusion-based video reconstruction is compatible with real-time, interactive, editable, streaming scenarios, turning high-quality video restoration into a practical component of modern vision systems.
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            <a href="https://www.alphaxiv.org/abs/2511.14203v1" target="_blank" rel="noopener noreferrer">
                用于海上船舶重识别的多尺度相关性感知Transformer
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Multi-Scale Correlation-Aware Transformer for Maritime Vessel Re-Identification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunhe Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然使用了Transformer架构，但其应用领域是海上船舶重识别，属于计算机视觉中的特定对象重识别任务。这与推荐系统、搜索或广告的核心领域没有直接关联，也没有展示出在异构数据处理方面的通用潜力。Transformer在此处仅作为视觉特征提取器使用，没有提供对推荐/搜索/广告领域有直接启发的技术进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 07:16:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14203v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14203v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Maritime vessel re-identification (Re-ID) plays a crucial role in advancing maritime monitoring and intelligent situational awareness systems. However, some existing vessel Re-ID methods are directly adapted from pedestrian-focused algorithms, making them ill-suited for mitigating the unique problems present in vessel images, particularly the greater intra-identity variations and more severe missing of local parts, which lead to the emergence of outlier samples within the same identity. To address these challenges, we propose the Multi-scale Correlation-aware Transformer Network (MCFormer), which explicitly models multi-scale correlations across the entire input set to suppress the adverse effects of outlier samples with intra-identity variations or local missing, incorporating two novel modules, the Global Correlation Module (GCM), and the Local Correlation Module (LCM). Specifically, GCM constructs a global similarity affinity matrix across all input images to model global correlations through feature aggregation based on inter-image consistency, rather than solely learning features from individual images as in most existing approaches. Simultaneously, LCM mines and aligns local features of positive samples with contextual similarity to extract local correlations by maintaining a dynamic memory bank, effectively compensating for missing or occluded regions in individual images. To further enhance feature robustness, MCFormer integrates global and local features that have been respectively correlated across multiple scales, effectively capturing latent relationships among image features. Experiments on three benchmarks demonstrate that MCFormer achieves state-of-the-art performance.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14184v1" target="_blank" rel="noopener noreferrer">
                GloTok：用于图像重建与生成的全局视角分词器
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            GloTok: Global Perspective Tokenizer for Image Reconstruction and Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xuan Zhao, Zhongyu Zhang, Yuge Huang, Yuxi Mi, Guodong Mu, Shouhong Ding, Jun Wa...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的图像重建与生成技术，属于纯粹的视觉研究方向。虽然标题中提到'Tokenizer'概念，但这与推荐系统、搜索或广告中的文本处理或异构数据建模没有直接关联。该技术缺乏在RecSys/Search/Ads领域的明确应用潜力，仅能获得较低相关性评分。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 06:40:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14184v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14184v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Existing state-of-the-art image tokenization methods leverage diverse semantic features from pre-trained vision models for additional supervision, to expand the distribution of latent representations and thereby improve the quality of image reconstruction and generation. These methods employ a locally supervised approach for semantic supervision, which limits the uniformity of semantic distribution. However, VA-VAE proves that a more uniform feature distribution yields better generation performance. In this work, we introduce a Global Perspective Tokenizer (GloTok), which utilizes global relational information to model a more uniform semantic distribution of tokenized features. Specifically, a codebook-wise histogram relation learning method is proposed to transfer the semantics, which are modeled by pre-trained models on the entire dataset, to the semantic codebook. Then, we design a residual learning module that recovers the fine-grained details to minimize the reconstruction error caused by quantization. Through the above design, GloTok delivers more uniformly distributed semantic latent representations, which facilitates the training of autoregressive (AR) models for generating high-quality images without requiring direct access to pre-trained models during the training process. Experiments on the standard ImageNet-1k benchmark clearly show that our proposed method achieves state-of-the-art reconstruction performance and generation quality.
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            <a href="https://www.alphaxiv.org/abs/2511.14183v1" target="_blank" rel="noopener noreferrer">
                UniSER：用于统一软效应去除的基础模型
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            UniSER: A Foundation Model for Unified Soft Effects Removal
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jingdong Zhang, Lingzhi Zhang, Qing Liu, Mang Tik Chiu, Connelly Barnes, Yizhou ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于图像处理中的软效应去除，这属于纯粹的计算机视觉任务。虽然标题提到'基础模型'，但软效应去除与推荐系统、搜索或广告中的核心问题（如排序、匹配或用户建模）没有直接关联。该技术缺乏在RecSys/Search/Ads领域的明显应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 06:39:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14183v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14183v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Digital images are often degraded by soft effects such as lens flare, haze, shadows, and reflections, which reduce aesthetics even though the underlying pixels remain partially visible. The prevailing works address these degradations in isolation, developing highly specialized, specialist models that lack scalability and fail to exploit the shared underlying essences of these restoration problems. While specialist models are limited, recent large-scale pretrained generalist models offer powerful, text-driven image editing capabilities. while recent general-purpose systems (e.g., GPT-4o, Flux Kontext, Nano Banana) require detailed prompts and often fail to achieve robust removal on these fine-grained tasks or preserve identity of the scene. Leveraging the common essence of soft effects, i.e., semi-transparent occlusions, we introduce a foundational versatile model UniSER, capable of addressing diverse degradations caused by soft effects within a single framework. Our methodology centers on curating a massive 3.8M-pair dataset to ensure robustness and generalization, which includes novel, physically-plausible data to fill critical gaps in public benchmarks, and a tailored training pipeline that fine-tunes a Diffusion Transformer to learn robust restoration priors from this diverse data, integrating fine-grained mask and strength controls. This synergistic approach allows UniSER to significantly outperform both specialist and generalist models, achieving robust, high-fidelity restoration in the wild.
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            <a href="https://www.alphaxiv.org/abs/2511.14159v1" target="_blank" rel="noopener noreferrer">
                MVI-Bench：评估大型视觉语言模型对误导性视觉输入鲁棒性的综合基准
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            <i class="fa fa-star mr-1"></i>2/10
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            MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huiyi Chen, Jiawei Peng, Dehai Min, Changchang Sun, Kaijie Chen, Yan Yan, Xu Yan...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视觉语言模型的评估基准，属于纯粹的评估基准研究范畴，与当前关注的推荐系统、搜索、广告核心进展或使能技术无关。虽然视觉语言模型在多模态领域有应用，但该论文仅关注评估基准而非技术进展或实际应用，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 05:48:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14159v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14159v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Evaluating the robustness of Large Vision-Language Models (LVLMs) is essential for their continued development and responsible deployment in real-world applications. However, existing robustness benchmarks typically focus on hallucination or misleading textual inputs, while largely overlooking the equally critical challenge posed by misleading visual inputs in assessing visual understanding. To fill this important gap, we introduce MVI-Bench, the first comprehensive benchmark specially designed for evaluating how Misleading Visual Inputs undermine the robustness of LVLMs. Grounded in fundamental visual primitives, the design of MVI-Bench centers on three hierarchical levels of misleading visual inputs: Visual Concept, Visual Attribute, and Visual Relationship. Using this taxonomy, we curate six representative categories and compile 1,248 expertly annotated VQA instances. To facilitate fine-grained robustness evaluation, we further introduce MVI-Sensitivity, a novel metric that characterizes LVLM robustness at a granular level. Empirical results across 18 state-of-the-art LVLMs uncover pronounced vulnerabilities to misleading visual inputs, and our in-depth analyses on MVI-Bench provide actionable insights that can guide the development of more reliable and robust LVLMs. The benchmark and codebase can be accessed at https://github.com/chenyil6/MVI-Bench.
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            <a href="https://www.alphaxiv.org/abs/2511.14120v1" target="_blank" rel="noopener noreferrer">
                基于视觉语言模型的多视角相位感知行人-车辆事故推理框架
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Multi-view Phase-aware Pedestrian-Vehicle Incident Reasoning Framework with Vision-Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hao Zhen, Yunxiang Yang, Jidong J. Yang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然该论文涉及视觉语言模型技术，但其应用场景（行人-车辆事故推理）属于交通安全领域，与推荐系统、搜索或广告的核心业务无关。论文标题表明这是一个计算机视觉应用，没有显示出在推荐、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 04:12:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14120v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14120v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Pedestrian-vehicle incidents remain a critical urban safety challenge, with pedestrians accounting for over 20% of global traffic fatalities. Although existing video-based systems can detect when incidents occur, they provide little insight into how these events unfold across the distinct cognitive phases of pedestrian behavior. Recent vision-language models (VLMs) have shown strong potential for video understanding, but they remain limited in that they typically process videos in isolation, without explicit temporal structuring or multi-view integration. This paper introduces Multi-view Phase-aware Pedestrian-Vehicle Incident Reasoning (MP-PVIR), a unified framework that systematically processes multi-view video streams into structured diagnostic reports through four stages: (1) event-triggered multi-view video acquisition, (2) pedestrian behavior phase segmentation, (3) phase-specific multi-view reasoning, and (4) hierarchical synthesis and diagnostic reasoning. The framework operationalizes behavioral theory by automatically segmenting incidents into cognitive phases, performing synchronized multi-view analysis within each phase, and synthesizing results into causal chains with targeted prevention strategies. Particularly, two specialized VLMs underpin the MP-PVIR pipeline: TG-VLM for behavioral phase segmentation (mIoU = 0.4881) and PhaVR-VLM for phase-aware multi-view analysis, achieving a captioning score of 33.063 and up to 64.70% accuracy on question answering. Finally, a designated large language model is used to generate comprehensive reports detailing scene understanding, behavior interpretation, causal reasoning, and prevention recommendations. Evaluation on the Woven Traffic Safety dataset shows that MP-PVIR effectively translates multi-view video data into actionable insights, advancing AI-driven traffic safety analytics for vehicle-infrastructure cooperative systems.
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            <a href="https://www.alphaxiv.org/abs/2511.14113v1" target="_blank" rel="noopener noreferrer">
                Coffee：可控扩散微调
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Coffee: Controllable Diffusion Fine-tuning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ziyao Zeng, Jingcheng Ni, Ruyi Liu, Alex Wong
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文主要关注扩散模型的可控微调，属于AIGC和内容生成领域。虽然扩散模型在图像生成方面有广泛应用，但论文标题没有显示出与推荐系统、搜索或广告排名的直接关联。可控生成技术可能在某些边缘场景有应用，但不符合当前关注的核心技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 03:53:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14113v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14113v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Text-to-image diffusion models can generate diverse content with flexible prompts, which makes them well-suited for customization through fine-tuning with a small amount of user-provided data. However, controllable fine-tuning that prevents models from learning undesired concepts present in the fine-tuning data, and from entangling those concepts with user prompts, remains an open challenge. It is crucial for downstream tasks like bias mitigation, preventing malicious adaptation, attribute disentanglement, and generalizable fine-tuning of diffusion policy. We propose Coffee that allows using language to specify undesired concepts to regularize the adaptation process. The crux of our method lies in keeping the embeddings of the user prompt from aligning with undesired concepts. Crucially, Coffee requires no additional training and enables flexible modification of undesired concepts by modifying textual descriptions. We evaluate Coffee by fine-tuning on images associated with user prompts paired with undesired concepts. Experimental results demonstrate that Coffee can prevent text-to-image models from learning specified undesired concepts during fine-tuning and outperforms existing methods. Code will be released upon acceptance.
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            <a href="https://www.alphaxiv.org/abs/2511.14111v1" target="_blank" rel="noopener noreferrer">
                CascadedViT：级联分块前馈与级联分组注意力视觉Transformer
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            CascadedViT: Cascaded Chunk-FeedForward and Cascaded Group Attention Vision Transformer
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Srivathsan Sivakumar, Faisal Z. Qureshi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉Transformer架构的效率改进，属于纯粹的计算机视觉领域研究。虽然Transformer架构在推荐和搜索系统中也有应用，但该工作针对视觉特定的优化（分块前馈和分组注意力）在推荐/搜索/广告领域的直接应用潜力有限，且没有明确展示如何将这些技术适配到文本或序列建模场景中。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 03:51:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14111v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14111v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vision Transformers (ViTs) have demonstrated remarkable performance across a range of computer vision tasks; however, their high computational, memory, and energy demands hinder deployment on resource-constrained platforms. In this paper, we propose \emph{Cascaded-ViT (CViT)}, a lightweight and compute-efficient vision transformer architecture featuring a novel feedforward network design called \emph{Cascaded-Chunk Feed Forward Network (CCFFN)}. By splitting input features, CCFFN improves parameter and FLOP efficiency without sacrificing accuracy. Experiments on ImageNet-1K show that our \emph{CViT-XL} model achieves 75.5\% Top-1 accuracy while reducing FLOPs by 15\% and energy consumption by 3.3\% compared to EfficientViT-M5. Across various model sizes, the CViT family consistently exhibits the lowest energy consumption, making it suitable for deployment on battery-constrained devices such as mobile phones and drones. Furthermore, when evaluated using a new metric called \emph{Accuracy-Per-FLOP (APF)}, which quantifies compute efficiency relative to accuracy, CViT models consistently achieve top-ranking efficiency. Particularly, CViT-L is 2.2\% more accurate than EfficientViT-M2 while having comparable APF scores.
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            <a href="https://www.alphaxiv.org/abs/2511.14109v1" target="_blank" rel="noopener noreferrer">
                A²GC：具有几何约束的非对称聚合用于局部聚合描述符
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            <i class="fa fa-star mr-1"></i>2/10
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            $A^2$GC: $A$symmetric $A$ggregation with Geometric Constraints for Locally Aggregated Descriptors
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhenyu Li, Tianyi Shang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的局部描述符聚合方法，涉及几何约束和非对称聚合技术。虽然标题暗示了聚合方法的研究，但没有明确表明与推荐系统、搜索或广告的直接关联。该技术可能适用于图像检索等视觉搜索场景，但缺乏明确的跨模态或序列建模应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 03:47:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14109v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14109v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Visual Place Recognition (VPR) aims to match query images against a database using visual cues. State-of-the-art methods aggregate features from deep backbones to form global descriptors. Optimal transport-based aggregation methods reformulate feature-to-cluster assignment as a transport problem, but the standard Sinkhorn algorithm symmetrically treats source and target marginals, limiting effectiveness when image features and cluster centers exhibit substantially different distributions. We propose an asymmetric aggregation VPR method with geometric constraints for locally aggregated descriptors, called $A^2$GC-VPR. Our method employs row-column normalization averaging with separate marginal calibration, enabling asymmetric matching that adapts to distributional discrepancies in visual place recognition. Geometric constraints are incorporated through learnable coordinate embeddings, computing compatibility scores fused with feature similarities, thereby promoting spatially proximal features to the same cluster and enhancing spatial awareness. Experimental results on MSLS, NordLand, and Pittsburgh datasets demonstrate superior performance, validating the effectiveness of our approach in improving matching accuracy and robustness.
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            <a href="https://www.alphaxiv.org/abs/2511.14099v1" target="_blank" rel="noopener noreferrer">
                FAPE-IR：面向一体化图像恢复的频率感知规划与执行框架
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            FAPE-IR: Frequency-Aware Planning and Execution Framework for All-in-One Image Restoration
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jingren Liu, Shuning Xu, Qirui Yang, Yun Wang, Xiangyu Chen, Zhong Ji
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像恢复技术，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。虽然图像在电商搜索中可能作为商品展示的一部分，但该论文的焦点是图像质量修复而非内容理解或排序，因此潜在应用价值有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 03:33:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14099v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14099v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    All-in-One Image Restoration (AIO-IR) aims to develop a unified model that can handle multiple degradations under complex conditions. However, existing methods often rely on task-specific designs or latent routing strategies, making it hard to adapt to real-world scenarios with various degradations. We propose FAPE-IR, a Frequency-Aware Planning and Execution framework for image restoration. It uses a frozen Multimodal Large Language Model (MLLM) as a planner to analyze degraded images and generate concise, frequency-aware restoration plans. These plans guide a LoRA-based Mixture-of-Experts (LoRA-MoE) module within a diffusion-based executor, which dynamically selects high- or low-frequency experts, complemented by frequency features of the input image. To further improve restoration quality and reduce artifacts, we introduce adversarial training and a frequency regularization loss. By coupling semantic planning with frequency-based restoration, FAPE-IR offers a unified and interpretable solution for all-in-one image restoration. Extensive experiments show that FAPE-IR achieves state-of-the-art performance across seven restoration tasks and exhibits strong zero-shot generalization under mixed degradations.
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            <a href="https://www.alphaxiv.org/abs/2511.14685v1" target="_blank" rel="noopener noreferrer">
                大型语言模型生成摘要中天体物理信息的编码与理解
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            Encoding and Understanding Astrophysical Information in Large Language Model-Generated Summaries
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kiera McCormick, Rafael Martínez-Galarza
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于天体物理学领域的LLM摘要生成，这属于明确的无关主题范畴。论文标题表明其研究的是特定科学领域（天体物理）的摘要生成应用，与推荐系统、搜索或广告的核心技术进展没有任何关联。没有证据表明该研究在编码技术或理解方法上有任何可迁移到推荐/搜索/广告领域的潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:23:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14685v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14685v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">astro-ph.IM</span></div>
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                    Large Language Models have demonstrated the ability to generalize well at many levels across domains, modalities, and even shown in-context learning capabilities. This enables research questions regarding how they can be used to encode physical information that is usually only available from scientific measurements, and loosely encoded in textual descriptions. Using astrophysics as a test bed, we investigate if LLM embeddings can codify physical summary statistics that are obtained from scientific measurements through two main questions: 1) Does prompting play a role on how those quantities are codified by the LLM? and 2) What aspects of language are most important in encoding the physics represented by the measurement? We investigate this using sparse autoencoders that extract interpretable features from the text.
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            <a href="https://www.alphaxiv.org/abs/2511.14683v1" target="_blank" rel="noopener noreferrer">
                赫普斯定律的二次项修正
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        <div class="mb-2 text-base text-gray-700">
            Quadratic Term Correction on Heaps' Law
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Oscar Fontanelli, Wentian Li
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及赫普斯定律的数学修正，这是关于词汇增长与语料规模关系的语言统计规律。该主题与推荐系统、搜索或广告的核心技术进展、LLM技术或Transformer架构没有直接关联，属于纯粹的语言统计理论研究，没有明确的实际应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:22:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14683v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14683v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Heaps' or Herdan's law characterizes the word-type vs. word-token relation by a power-law function, which is concave in linear-linear scale but a straight line in log-log scale. However, it has been observed that even in log-log scale, the type-token curve is still slightly concave, invalidating the power-law relation. At the next-order approximation, we have shown, by twenty English novels or writings (some are translated from another language to English), that quadratic functions in log-log scale fit the type-token data perfectly. Regression analyses of log(type)-log(token) data with both a linear and quadratic term consistently lead to a linear coefficient of slightly larger than 1, and a quadratic coefficient around -0.02. Using the ``random drawing colored ball from the bag with replacement" model, we have shown that the curvature of the log-log scale is identical to a ``pseudo-variance" which is negative. Although a pseudo-variance calculation may encounter numeric instability when the number of tokens is large, due to the large values of pseudo-weights, this formalism provides a rough estimation of the curvature when the number of tokens is small.
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            <a href="https://www.alphaxiv.org/abs/2511.14642v1" target="_blank" rel="noopener noreferrer">
                比较性幻觉的强度分级可通过贝叶斯推断解释
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            Graded strength of comparative illusions is explained by Bayesian inference
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuhan Zhang, Erxiao Wang, Cory Shain
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及认知心理学中的比较性幻觉和贝叶斯推断，属于纯理论认知科学领域。没有显示出与推荐系统、搜索、广告或相关使能技术（如LLM、Transformer架构）的任何潜在应用关联。内容完全落在无关主题范围内，特别是纯粹理论论文且缺乏实际应用意义。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 16:33:19
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                <a href="https://arxiv.org/abs/2511.14642v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14642v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Like visual processing, language processing is susceptible to illusions in which people systematically misperceive stimuli. In one such case--the comparative illusion (CI), e.g., More students have been to Russia than I have--comprehenders tend to judge the sentence as acceptable despite its underlying nonsensical comparison. Prior research has argued that this phenomenon can be explained as Bayesian inference over a noisy channel: the posterior probability of an interpretation of a sentence is proportional to both the prior probability of that interpretation and the likelihood of corruption into the observed (CI) sentence. Initial behavioral work has supported this claim by evaluating a narrow set of alternative interpretations of CI sentences and showing that comprehenders favor interpretations that are more likely to have been corrupted into the illusory sentence. In this study, we replicate and go substantially beyond this earlier work by directly predicting the strength of illusion with a quantitative model of the posterior probability of plausible interpretations, which we derive through a novel synthesis of statistical language models with human behavioral data. Our model explains not only the fine gradations in the strength of CI effects, but also a previously unexplained effect caused by pronominal vs. full noun phrase than-clause subjects. These findings support a noisy-channel theory of sentence comprehension by demonstrating that the theory makes novel predictions about the comparative illusion that bear out empirically. This outcome joins related evidence of noisy channel processing in both illusory and non-illusory contexts to support noisy channel inference as a unified computational-level theory of diverse language processing phenomena.
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            <a href="https://www.alphaxiv.org/abs/2511.14638v1" target="_blank" rel="noopener noreferrer">
                用于罕见疾病临床推理与诊断的专用大语言模型
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        <div class="mb-2 text-base text-gray-700">
            A Specialized Large Language Model for Clinical Reasoning and Diagnosis in Rare Diseases
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tao Yang, Dandan Huang, Yunting Lin, Pengfei Wu, Zhikun Wu, Gangyuan Ma, Yulan L...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的临床推理和诊断应用，属于明确的医疗生物学领域，这在无关主题中被明确排除。虽然涉及大语言模型技术，但其应用场景与推荐系统、搜索或广告完全无关，没有任何潜在的应用关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 16:29:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14638v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14638v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Rare diseases affect hundreds of millions worldwide, yet diagnosis often spans years. Convectional pipelines decouple noisy evidence extraction from downstream inferential diagnosis, and general/medical large language models (LLMs) face scarce real world electronic health records (EHRs), stale domain knowledge, and hallucinations. We assemble a large, domain specialized clinical corpus and a clinician validated reasoning set, and develop RareSeek R1 via staged instruction tuning, chain of thought learning, and graph grounded retrieval. Across multicenter EHR narratives and public benchmarks, RareSeek R1 attains state of the art accuracy, robust generalization, and stability under noisy or overlapping phenotypes. Augmented retrieval yields the largest gains when narratives pair with prioritized variants by resolving ambiguity and aligning candidates to mechanisms. Human studies show performance on par with experienced physicians and consistent gains in assistive use. Notably, transparent reasoning highlights decisive non phenotypic evidence (median 23.1%, such as imaging, interventions, functional tests) underpinning many correct diagnoses. This work advances a narrative first, knowledge integrated reasoning paradigm that shortens the diagnostic odyssey and enables auditable, clinically translatable decision support.
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            <a href="https://www.alphaxiv.org/abs/2511.14603v1" target="_blank" rel="noopener noreferrer">
                一种从急性肾损伤到慢性肾脏疾病的疾病进展特征化方法
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            A Method for Characterizing Disease Progression from Acute Kidney Injury to Chronic Kidney Disease
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yilu Fang, Jordan G. Nestor, Casey N. Ta, Jerard Z. Kneifati-Hayek, Chunhua Weng
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学领域的疾病进展分析，特别是肾脏疾病的病理学研究。这完全属于被排除的医学/生物学特定领域应用范畴，与推荐系统、搜索、广告或相关LLM技术没有任何关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 15:53:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14603v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14603v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    Patients with acute kidney injury (AKI) are at high risk of developing chronic kidney disease (CKD), but identifying those at greatest risk remains challenging. We used electronic health record (EHR) data to dynamically track AKI patients' clinical evolution and characterize AKI-to-CKD progression. Post-AKI clinical states were identified by clustering patient vectors derived from longitudinal medical codes and creatinine measurements. Transition probabilities between states and progression to CKD were estimated using multi-state modeling. After identifying common post-AKI trajectories, CKD risk factors in AKI subpopulations were identified through survival analysis. Of 20,699 patients with AKI at admission, 3,491 (17%) developed CKD. We identified fifteen distinct post-AKI states, each with different probabilities of CKD development. Most patients (75%, n=15,607) remained in a single state or made only one transition during the study period. Both established (e.g., AKI severity, diabetes, hypertension, heart failure, liver disease) and novel CKD risk factors, with their impact varying across these clinical states. This study demonstrates a data-driven approach for identifying high-risk AKI patients, supporting the development of decision-support tools for early CKD detection and intervention.
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            <a href="https://www.alphaxiv.org/abs/2511.14598v1" target="_blank" rel="noopener noreferrer">
                利用数字化报纸收集低资源语言的摘要数据
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            Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Noam Dahan, Omer Kidron, Gabriel Stanovsky
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于低资源语言的摘要数据收集，属于纯粹的NLP数据收集和摘要任务，与推荐系统、搜索或广告的核心技术无关。数字化报纸作为数据源不涉及任何推荐、搜索或广告的建模技术，也没有展示出在相关领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 15:39:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14598v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14598v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    High quality summarization data remains scarce in under-represented languages. However, historical newspapers, made available through recent digitization efforts, offer an abundant source of untapped, naturally annotated data. In this work, we present a novel method for collecting naturally occurring summaries via Front-Page Teasers, where editors summarize full length articles. We show that this phenomenon is common across seven diverse languages and supports multi-document summarization. To scale data collection, we develop an automatic process, suited to varying linguistic resource levels. Finally, we apply this process to a Hebrew newspaper title, producing HEBTEASESUM, the first dedicated multi-document summarization dataset in Hebrew.
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            <a href="https://www.alphaxiv.org/abs/2511.14566v1" target="_blank" rel="noopener noreferrer">
                捷克语和斯洛伐克语文档级声明提取的指标研究
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            Examining the Metrics for Document-Level Claim Extraction in Czech and Slovak
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lucia Makaiová, Martin Fajčík, Antonín Jarolím
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于捷克语和斯洛伐克语中声明提取的指标评估，这属于特定语言的信息提取任务。该主题与推荐系统、搜索或广告的核心进展、LLM技术、Transformer架构或异构数据建模没有明显关联。它更偏向于特定语言的NLP评估，属于应排除的纯NLP中心主题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 15:09:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14566v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14566v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Document-level claim extraction remains an open challenge in the field of fact-checking, and subsequently, methods for evaluating extracted claims have received limited attention. In this work, we explore approaches to aligning two sets of claims pertaining to the same source document and computing their similarity through an alignment score. We investigate techniques to identify the best possible alignment and evaluation method between claim sets, with the aim of providing a reliable evaluation framework. Our approach enables comparison between model-extracted and human-annotated claim sets, serving as a metric for assessing the extraction performance of models and also as a possible measure of inter-annotator agreement. We conduct experiments on newly collected dataset-claims extracted from comments under Czech and Slovak news articles-domains that pose additional challenges due to the informal language, strong local context, and subtleties of these closely related languages. The results draw attention to the limitations of current evaluation approaches when applied to document-level claim extraction and highlight the need for more advanced methods-ones able to correctly capture semantic similarity and evaluate essential claim properties such as atomicity, checkworthiness, and decontextualization.
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            <a href="https://www.alphaxiv.org/abs/2511.14445v1" target="_blank" rel="noopener noreferrer">
                告诉我：一个基于大语言模型的心理健康助手，具备检索增强生成、合成对话生成和智能体规划功能
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            Tell Me: An LLM-powered Mental Well-being Assistant with RAG, Synthetic Dialogue Generation, and Agentic Planning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Trishala Jayesh Ahalpara
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于心理健康应用领域，属于医疗/健康领域的特定应用，与推荐系统、搜索或广告完全无关。论文描述的是一个专门的心理健康助手，不涉及任何推荐、搜索或广告相关的技术或应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 12:43:04
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                <a href="https://arxiv.org/abs/2511.14445v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14445v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.HC</span><span class="category-tag">cs.LG</span></div>
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                    We present Tell Me, a mental well-being system that leverages advances in large language models to provide accessible, context-aware support for users and researchers. The system integrates three components: (i) a retrieval-augmented generation (RAG) assistant for personalized, knowledge-grounded dialogue; (ii) a synthetic client-therapist dialogue generator conditioned on client profiles to facilitate research on therapeutic language and data augmentation; and (iii) a Well-being AI crew, implemented with CrewAI, that produces weekly self-care plans and guided meditation audio. The system is designed as a reflective space for emotional processing rather than a substitute for professional therapy. It illustrates how conversational assistants can lower barriers to support, complement existing care, and broaden access to mental health resources. To address the shortage of confidential therapeutic data, we introduce synthetic client-therapist dialogue generation conditioned on client profiles. Finally, the planner demonstrates an innovative agentic workflow for dynamically adaptive, personalized self-care, bridging the limitations of static well-being tools. We describe the architecture, demonstrate its functionalities, and report evaluation of the RAG assistant in curated well-being scenarios using both automatic LLM-based judgments and a human-user study. This work highlights opportunities for interdisciplinary collaboration between NLP researchers and mental health professionals to advance responsible innovation in human-AI interaction for well-being.
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            <a href="https://www.alphaxiv.org/abs/2511.14439v1" target="_blank" rel="noopener noreferrer">
                MedBench v4：一个用于评估中文医学语言模型、多模态模型和智能代理的鲁棒且可扩展的基准
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            MedBench v4: A Robust and Scalable Benchmark for Evaluating Chinese Medical Language Models, Multimodal Models, and Intelligent Agents
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jinru Ding, Lu Lu, Chao Ding, Mouxiao Bian, Jiayuan Chen, Renjie Lu, Wenrao Pang...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的评估基准，属于明确的医学应用范畴，这在无关主题中被明确排除。虽然涉及语言模型和多模态模型，但其特定于医学领域的性质使其与推荐系统、搜索或广告的核心技术进展无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 12:37:32
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                <a href="https://arxiv.org/abs/2511.14439v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14439v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in medical large language models (LLMs), multimodal models, and agents demand evaluation frameworks that reflect real clinical workflows and safety constraints. We present MedBench v4, a nationwide, cloud-based benchmarking infrastructure comprising over 700,000 expert-curated tasks spanning 24 primary and 91 secondary specialties, with dedicated tracks for LLMs, multimodal models, and agents. Items undergo multi-stage refinement and multi-round review by clinicians from more than 500 institutions, and open-ended responses are scored by an LLM-as-a-judge calibrated to human ratings. We evaluate 15 frontier models. Base LLMs reach a mean overall score of 54.1/100 (best: Claude Sonnet 4.5, 62.5/100), but safety and ethics remain low (18.4/100). Multimodal models perform worse overall (mean 47.5/100; best: GPT-5, 54.9/100), with solid perception yet weaker cross-modal reasoning. Agents built on the same backbones substantially improve end-to-end performance (mean 79.8/100), with Claude Sonnet 4.5-based agents achieving up to 85.3/100 overall and 88.9/100 on safety tasks. MedBench v4 thus reveals persisting gaps in multimodal reasoning and safety for base models, while showing that governance-aware agentic orchestration can markedly enhance benchmarked clinical readiness without sacrificing capability. By aligning tasks with Chinese clinical guidelines and regulatory priorities, the platform offers a practical reference for hospitals, developers, and policymakers auditing medical AI.
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            <a href="https://www.alphaxiv.org/abs/2511.14423v1" target="_blank" rel="noopener noreferrer">
                针对教育领域大语言模型越狱与微调攻击的统一防御
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            Unified Defense for Large Language Models against Jailbreak and Fine-Tuning Attacks in Education
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xin Yi, Yue Li, Dongsheng Shi, Linlin Wang, Xiaoling Wang, Liang He
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注LLM安全防御（越狱和微调攻击），属于安全/隐私范畴，这在无关主题中被明确排除。论文的教育领域应用也属于特定领域应用，与推荐系统、搜索或广告的核心技术进展无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 12:27:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14423v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14423v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) are increasingly integrated into educational applications. However, they remain vulnerable to jailbreak and fine-tuning attacks, which can compromise safety alignment and lead to harmful outputs. Existing studies mainly focus on general safety evaluations, with limited attention to the unique safety requirements of educational scenarios. To address this gap, we construct EduHarm, a benchmark containing safe-unsafe instruction pairs across five representative educational scenarios, enabling systematic safety evaluation of educational LLMs. Furthermore, we propose a three-stage shield framework (TSSF) for educational LLMs that simultaneously mitigates both jailbreak and fine-tuning attacks. First, safety-aware attention realignment redirects attention toward critical unsafe tokens, thereby restoring the harmfulness feature that discriminates between unsafe and safe inputs. Second, layer-wise safety judgment identifies harmfulness features by aggregating safety cues across multiple layers to detect unsafe instructions. Finally, defense-driven dual routing separates safe and unsafe queries, ensuring normal processing for benign inputs and guarded responses for harmful ones. Extensive experiments across eight jailbreak attack strategies demonstrate that TSSF effectively strengthens safety while preventing over-refusal of benign queries. Evaluations on three fine-tuning attack datasets further show that it consistently achieves robust defense against harmful queries while maintaining preserving utility gains from benign fine-tuning.
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            <a href="https://www.alphaxiv.org/abs/2511.14366v1" target="_blank" rel="noopener noreferrer">
                ATLAS：面向前沿科学推理的高难度、多学科基准
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            ATLAS: A High-Difficulty, Multidisciplinary Benchmark for Frontier Scientific Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hongwei Liu, Junnan Liu, Shudong Liu, Haodong Duan, Yuqiang Li, Mao Su, Xiaohong...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于科学推理基准的构建，属于纯粹的评估基准研究，与推荐系统、搜索或广告的核心技术进展无关。虽然基准测试可能间接影响模型开发，但论文本身不涉及任何推荐系统架构、Transformer改进或LLM在搜索广告中的直接应用，完全属于评估基准的范畴。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 11:13:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14366v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14366v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    The rapid advancement of Large Language Models (LLMs) has led to performance saturation on many established benchmarks, questioning their ability to distinguish frontier models. Concurrently, existing high-difficulty benchmarks often suffer from narrow disciplinary focus, oversimplified answer formats, and vulnerability to data contamination, creating a fidelity gap with real-world scientific inquiry. To address these challenges, we introduce ATLAS (AGI-Oriented Testbed for Logical Application in Science), a large-scale, high-difficulty, and cross-disciplinary evaluation suite composed of approximately 800 original problems. Developed by domain experts (PhD-level and above), ATLAS spans seven core scientific fields: mathematics, physics, chemistry, biology, computer science, earth science, and materials science. Its key features include: (1) High Originality and Contamination Resistance, with all questions newly created or substantially adapted to prevent test data leakage; (2) Cross-Disciplinary Focus, designed to assess models' ability to integrate knowledge and reason across scientific domains; (3) High-Fidelity Answers, prioritizing complex, open-ended answers involving multi-step reasoning and LaTeX-formatted expressions over simple multiple-choice questions; and (4) Rigorous Quality Control, employing a multi-stage process of expert peer review and adversarial testing to ensure question difficulty, scientific value, and correctness. We also propose a robust evaluation paradigm using a panel of LLM judges for automated, nuanced assessment of complex answers. Preliminary results on leading models demonstrate ATLAS's effectiveness in differentiating their advanced scientific reasoning capabilities. We plan to develop ATLAS into a long-term, open, community-driven platform to provide a reliable "ruler" for progress toward Artificial General Intelligence.
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            <a href="https://www.alphaxiv.org/abs/2511.14365v1" target="_blank" rel="noopener noreferrer">
                分词瓶颈：词汇扩展如何改进预训练语言模型中的化学表示学习
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            The Tokenization Bottleneck: How Vocabulary Extension Improves Chemistry Representation Learning in Pretrained Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Prathamesh Kalamkar, Ned Letcher, Meissane Chami, Sahger Lad, Shayan Mohanty, Pr...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于化学领域的表示学习，这属于明确的无关主题（医学/生物学/化学应用）。虽然提到了预训练语言模型，但核心应用场景是化学领域，与推荐系统、搜索或广告没有直接关联。该研究缺乏在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 11:12:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14365v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14365v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    The application of large language models (LLMs) to chemistry is frequently hampered by a "tokenization bottleneck", where tokenizers tuned on general-domain text tend to fragment chemical representations such as SMILES into semantically uninformative sub-tokens. This paper introduces a principled methodology to resolve this bottleneck by unifying the representation of natural language and molecular structures within a single model. Our approach involves targeted vocabulary extension-augmenting a pretrained LLM's vocabulary with chemically salient tokens, followed by continued pretraining on chemistry-domain text to integrate this new knowledge. We provide an empirical demonstration of the effectiveness of this strategy, showing that our methodology leads to superior performance on a range of downstream chemical tasks.
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            <a href="https://www.alphaxiv.org/abs/2511.14301v1" target="_blank" rel="noopener noreferrer">
                NLP中的隐写后门攻击：超低投毒率与防御规避
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            Steganographic Backdoor Attacks in NLP: Ultra-Low Poisoning and Defense Evasion
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Eric Xue, Ruiyi Zhang, Zijun Zhang, Pengtao Xie
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于NLP领域的安全攻击和防御机制，属于安全隐私范畴，这在无关主题中被明确排除。虽然涉及LLM技术，但核心关注点是后门攻击和防御规避，与推荐系统、搜索或广告中的核心进展、架构改进或直接应用无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 09:56:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14301v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14301v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Transformer models are foundational to natural language processing (NLP) applications, yet remain vulnerable to backdoor attacks introduced through poisoned data, which implant hidden behaviors during training. To strengthen the ability to prevent such compromises, recent research has focused on designing increasingly stealthy attacks to stress-test existing defenses, pairing backdoor behaviors with stylized artifact or token-level perturbation triggers. However, this trend diverts attention from the harder and more realistic case: making the model respond to semantic triggers such as specific names or entities, where a successful backdoor could manipulate outputs tied to real people or events in deployed systems. Motivated by this growing disconnect, we introduce SteganoBackdoor, bringing stealth techniques back into line with practical threat models. Leveraging innocuous properties from natural-language steganography, SteganoBackdoor applies a gradient-guided data optimization process to transform semantic trigger seeds into steganographic carriers that embed a high backdoor payload, remain fluent, and exhibit no representational resemblance to the trigger. Across diverse experimental settings, SteganoBackdoor achieves over 99% attack success at an order-of-magnitude lower data-poisoning rate than prior approaches while maintaining unparalleled evasion against a comprehensive suite of data-level defenses. By revealing this practical and covert attack, SteganoBackdoor highlights an urgent blind spot in current defenses and demands immediate attention to adversarial data defenses and real-world threat modeling.
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            <a href="https://www.alphaxiv.org/abs/2511.14295v1" target="_blank" rel="noopener noreferrer">
                AraLingBench：用于评估大型语言模型阿拉伯语语言学能力的人工标注基准
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            AraLingBench A Human-Annotated Benchmark for Evaluating Arabic Linguistic Capabilities of Large Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mohammad Zbib, Hasan Abed Al Kader Hammoud, Sina Mukalled, Nadine Rizk, Fatima K...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于阿拉伯语语言学能力的基准评估，属于纯粹的NLP评估基准范畴。虽然涉及LLM评估，但没有任何与推荐系统、搜索或广告相关的潜在应用，完全属于被排除的'评估基准'和'纯粹NLP中心话题'类别。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 09:47:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14295v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14295v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    We present AraLingBench: a fully human annotated benchmark for evaluating the Arabic linguistic competence of large language models (LLMs). The benchmark spans five core categories: grammar, morphology, spelling, reading comprehension, and syntax, through 150 expert-designed multiple choice questions that directly assess structural language understanding. Evaluating 35 Arabic and bilingual LLMs reveals that current models demonstrate strong surface level proficiency but struggle with deeper grammatical and syntactic reasoning. AraLingBench highlights a persistent gap between high scores on knowledge-based benchmarks and true linguistic mastery, showing that many models succeed through memorization or pattern recognition rather than authentic comprehension. By isolating and measuring fundamental linguistic skills, AraLingBench provides a diagnostic framework for developing Arabic LLMs. The full evaluation code is publicly available on GitHub.
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            <a href="https://www.alphaxiv.org/abs/2511.14255v1" target="_blank" rel="noopener noreferrer">
                AfriSpeech-MultiBench：面向非洲口音英语自动语音识别的垂直化多领域多国家基准套件
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            AfriSpeech-MultiBench: A Verticalized Multidomain Multicountry Benchmark Suite for African Accented English ASR
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gabrial Zencha Ashungafac, Mardhiyah Sanni, Busayo Awobade, Alex Gichamba, Tobi ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语音识别基准测试，属于语音处理领域，与搜索、推荐或广告系统的核心进展没有直接关联。虽然语音识别在语音搜索中有潜在应用，但该论文主要关注非洲口音英语的基准测试，属于特定领域的技术评估，不符合当前关注的LLM技术、Transformer架构进展或推荐系统核心算法等焦点领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 08:44:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14255v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14255v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Recent advances in speech-enabled AI, including Google's NotebookLM and OpenAI's speech-to-speech API, are driving widespread interest in voice interfaces globally. Despite this momentum, there exists no publicly available application-specific model evaluation that caters to Africa's linguistic diversity. We present AfriSpeech-MultiBench, the first domain-specific evaluation suite for over 100 African English accents across 10+ countries and seven application domains: Finance, Legal, Medical, General dialogue, Call Center, Named Entities and Hallucination Robustness. We benchmark a diverse range of open, closed, unimodal ASR and multimodal LLM-based speech recognition systems using both spontaneous and non-spontaneous speech conversation drawn from various open African accented English speech datasets. Our empirical analysis reveals systematic variation: open-source ASR models excels in spontaneous speech contexts but degrades on noisy, non-native dialogue; multimodal LLMs are more accent-robust yet struggle with domain-specific named entities; proprietary models deliver high accuracy on clean speech but vary significantly by country and domain. Models fine-tuned on African English achieve competitive accuracy with lower latency, a practical advantage for deployment, hallucinations still remain a big problem for most SOTA models. By releasing this comprehensive benchmark, we empower practitioners and researchers to select voice technologies suited to African use-cases, fostering inclusive voice applications for underserved communities.
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            <a href="https://www.alphaxiv.org/abs/2511.14245v1" target="_blank" rel="noopener noreferrer">
                MuCPT：音乐相关自然语言模型的持续预训练
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            MuCPT: Music-related Natural Language Model Continued Pretraining
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kai Tian, Yirong Mao, Wendong Bi, Hanjie Wang, Que Wenhui
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于音乐领域的自然语言模型持续预训练，属于特定领域（音乐）的LLM应用，与推荐系统、搜索或广告的核心技术进展无关。论文标题没有显示任何与异构数据处理、Transformer架构改进或RecSys/Search/Ads潜在应用相关的技术要素。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 08:33:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14245v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14245v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large language models perform strongly on general tasks but remain constrained in specialized settings such as music, particularly in the music-entertainment domain, where corpus scale, purity, and the match between data and training objectives are critical. We address this by constructing a large, music-related natural language corpus (40B tokens) that combines open source and in-house data, and by implementing a domain-first data pipeline: a lightweight classifier filters and weights in-domain text, followed by multi-stage cleaning, de-duplication, and privacy-preserving masking. We further integrate multi-source music text with associated metadata to form a broader, better-structured foundation of domain knowledge. On the training side, we introduce reference-model (RM)-based token-level soft scoring for quality control: a unified loss-ratio criterion is used both for data selection and for dynamic down-weighting during optimization, reducing noise gradients and amplifying task-aligned signals, thereby enabling more effective music-domain continued pretraining and alignment. To assess factuality, we design the MusicSimpleQA benchmark, which adopts short, single-answer prompts with automated agreement scoring. Beyond the benchmark design, we conduct systematic comparisons along the axes of data composition. Overall, this work advances both the right corpus and the right objective, offering a scalable data-training framework and a reusable evaluation tool for building domain LLMs in the music field.
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            <a href="https://www.alphaxiv.org/abs/2511.14230v1" target="_blank" rel="noopener noreferrer">
                ArbESC+：用于阿拉伯语语法错误校正的增强编辑选择系统组合方法——解决冲突并改进阿拉伯语GEC中的系统组合
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            ArbESC+: Arabic Enhanced Edit Selection System Combination for Grammatical Error Correction Resolving conflict and improving system combination in Arabic GEC
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ahlam Alrehili, Areej Alhothali
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于阿拉伯语语法错误校正（GEC）这一特定NLP任务，属于纯粹的语言处理领域。虽然涉及系统组合技术，但这是针对语法校正的专门应用，与推荐系统、搜索或广告的核心技术没有直接关联，也没有展示出在这些领域应用的潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 08:06:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14230v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14230v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Grammatical Error Correction (GEC) is an important aspect of natural language processing. Arabic has a complicated morphological and syntactic structure, posing a greater challenge than other languages. Even though modern neural models have improved greatly in recent years, the majority of previous attempts used individual models without taking into account the potential benefits of combining different systems. In this paper, we present one of the first multi-system approaches for correcting grammatical errors in Arabic, the Arab Enhanced Edit Selection System Complication (ArbESC+). Several models are used to collect correction proposals, which are represented as numerical features in the framework. A classifier determines and implements the appropriate corrections based on these features. In order to improve output quality, the framework uses support techniques to filter overlapping corrections and estimate decision reliability. A combination of AraT5, ByT5, mT5, AraBART, AraBART+Morph+GEC, and Text editing systems gave better results than a single model alone, with F0.5 at 82.63% on QALB-14 test data, 84.64% on QALB-15 L1 data, and 65.55% on QALB-15 L2 data. As one of the most significant contributions of this work, it's the first Arab attempt to integrate linguistic error correction. Improving existing models provides a practical step towards developing advanced tools that will benefit users and researchers of Arabic text processing.
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            <a href="https://www.alphaxiv.org/abs/2511.14172v1" target="_blank" rel="noopener noreferrer">
                SymLoc：在HaluEval和TruthfulQA数据集上对幻觉进行符号化定位
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            SymLoc: Symbolic Localization of Hallucination across HaluEval and TruthfulQA
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Naveen Lamba, Sanju Tiwari, Manas Gaur
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM幻觉的定位和评估，这属于纯粹的NLP评估基准范畴，与您的关注点无关。论文内容涉及幻觉检测和评估方法，没有展示在推荐系统、搜索或广告中的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 06:16:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14172v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14172v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    LLMs still struggle with hallucination, especially when confronted with symbolic triggers like modifiers, negation, numbers, exceptions, and named entities. Yet, we lack a clear understanding of where these symbolic hallucinations originate, making it crucial to systematically handle such triggers and localize the emergence of hallucination inside the model. While prior work explored localization using statistical techniques like LSC and activation variance analysis, these methods treat all tokens equally and overlook the role symbolic linguistic knowledge plays in triggering hallucinations. So far, no approach has investigated how symbolic elements specifically drive hallucination failures across model layers, nor has symbolic linguistic knowledge been used as the foundation for a localization framework. We propose the first symbolic localization framework that leverages symbolic linguistic and semantic knowledge to meaningfully trace the development of hallucinations across all model layers. By focusing on how models process symbolic triggers, we analyze five models using HaluEval and TruthfulQA. Our symbolic knowledge approach reveals that attention variance for these linguistic elements explodes to critical instability in early layers (2-4), with negation triggering catastrophic variance levels, demonstrating that symbolic semantic processing breaks down from the very beginning. Through the lens of symbolic linguistic knowledge, despite larger model sizes, hallucination rates remain consistently high (78.3%-83.7% across Gemma variants), with steep attention drops for symbolic semantic triggers throughout deeper layers. Our findings demonstrate that hallucination is fundamentally a symbolic linguistic processing failure, not a general generation problem, revealing that symbolic semantic knowledge provides the key to understanding and localizing hallucination mechanisms in LLMs.
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            <a href="https://www.alphaxiv.org/abs/2511.14112v1" target="_blank" rel="noopener noreferrer">
                罕见ICD代码的合成临床笔记：面向长尾医疗编码的数据中心框架
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            Synthetic Clinical Notes for Rare ICD Codes: A Data-Centric Framework for Long-Tail Medical Coding
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Truong Vo, Weiyi Wu, Kaize Ding
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的特定应用（ICD编码和临床笔记），属于明确的医疗领域应用，与搜索、推荐或广告系统无关。论文讨论的数据合成和长尾问题虽然在技术上可能有趣，但缺乏向RecSys/Search/Ads领域转化的明确路径或潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 03:52:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14112v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14112v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Automatic ICD coding from clinical text is a critical task in medical NLP but remains hindered by the extreme long-tail distribution of diagnostic codes. Thousands of rare and zero-shot ICD codes are severely underrepresented in datasets like MIMIC-III, leading to low macro-F1 scores. In this work, we propose a data-centric framework that generates high-quality synthetic discharge summaries to mitigate this imbalance. Our method constructs realistic multi-label code sets anchored on rare codes by leveraging real-world co-occurrence patterns, ICD descriptions, synonyms, taxonomy, and similar clinical notes. Using these structured prompts, we generate 90,000 synthetic notes covering 7,902 ICD codes, significantly expanding the training distribution. We fine-tune two state-of-the-art transformer-based models, PLM-ICD and GKI-ICD, on both the original and extended datasets. Experiments show that our approach modestly improves macro-F1 while maintaining strong micro-F1, outperforming prior SOTA. While the gain may seem marginal relative to the computational cost, our results demonstrate that carefully crafted synthetic data can enhance equity in long-tail ICD code prediction.
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            <a href="https://www.alphaxiv.org/abs/2511.14045v1" target="_blank" rel="noopener noreferrer">
                GRPO隐私面临风险：针对具有可验证奖励的强化学习的成员推理攻击
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            GRPO Privacy Is at Risk: A Membership Inference Attack Against Reinforcement Learning With Verifiable Rewards
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yule Liu, Heyi Zhang, Jinyi Zheng, Zhen Sun, Zifan Peng, Tianshuo Cong, Yilong Y...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于强化学习中的隐私攻击和成员推理，这属于明确排除的隐私和安全相关主题。虽然提到了强化学习，但论文的核心关注点是隐私风险而非推荐系统、搜索或广告的技术应用。该主题与用户当前关注的领域没有直接相关性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 01:51:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14045v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14045v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Membership inference attacks (MIAs) on large language models (LLMs) pose significant privacy risks across various stages of model training. Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have brought a profound paradigm shift in LLM training, particularly for complex reasoning tasks. However, the on-policy nature of RLVR introduces a unique privacy leakage pattern: since training relies on self-generated responses without fixed ground-truth outputs, membership inference must now determine whether a given prompt (independent of any specific response) is used during fine-tuning. This creates a threat where leakage arises not from answer memorization. To audit this novel privacy risk, we propose Divergence-in-Behavior Attack (DIBA), the first membership inference framework specifically designed for RLVR. DIBA shifts the focus from memorization to behavioral change, leveraging measurable shifts in model behavior across two axes: advantage-side improvement (e.g., correctness gain) and logit-side divergence (e.g., policy drift). Through comprehensive evaluations, we demonstrate that DIBA significantly outperforms existing baselines, achieving around 0.8 AUC and an order-of-magnitude higher TPR@0.1%FPR. We validate DIBA's superiority across multiple settings--including in-distribution, cross-dataset, cross-algorithm, black-box scenarios, and extensions to vision-language models. Furthermore, our attack remains robust under moderate defensive measures. To the best of our knowledge, this is the first work to systematically analyze privacy vulnerabilities in RLVR, revealing that even in the absence of explicit supervision, training data exposure can be reliably inferred through behavioral traces.
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            <a href="https://www.alphaxiv.org/abs/2511.14761v1" target="_blank" rel="noopener noreferrer">
                ARC是一个视觉问题！
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            ARC Is a Vision Problem!
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Keya Hu, Ali Cy, Linlu Qiu, Xiaoman Delores Ding, Runqian Wang, Yeyin Eva Zhu, J...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该标题明确指向视觉问题领域，与纯粹的计算机视觉相关，没有表明与推荐系统、搜索或广告有任何联系。根据筛选标准，纯粹的视觉论文如果没有明确的RecSys/Search/Ads相关性，应被视为不相关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 18:59:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14761v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14761v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The Abstraction and Reasoning Corpus (ARC) is designed to promote research on abstract reasoning, a fundamental aspect of human intelligence. Common approaches to ARC treat it as a language-oriented problem, addressed by large language models (LLMs) or recurrent reasoning models. However, although the puzzle-like tasks in ARC are inherently visual, existing research has rarely approached the problem from a vision-centric perspective. In this work, we formulate ARC within a vision paradigm, framing it as an image-to-image translation problem. To incorporate visual priors, we represent the inputs on a "canvas" that can be processed like natural images. It is then natural for us to apply standard vision architectures, such as a vanilla Vision Transformer (ViT), to perform image-to-image mapping. Our model is trained from scratch solely on ARC data and generalizes to unseen tasks through test-time training. Our framework, termed Vision ARC (VARC), achieves 60.4% accuracy on the ARC-1 benchmark, substantially outperforming existing methods that are also trained from scratch. Our results are competitive with those of leading LLMs and close the gap to average human performance.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14760v1" target="_blank" rel="noopener noreferrer">
                UniGen-1.5：通过强化学习中的奖励统一增强图像生成与编辑
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            UniGen-1.5: Enhancing Image Generation and Editing through Reward Unification in Reinforcement Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rui Tian, Mingfei Gao, Haiming Gang, Jiasen Lu, Zhe Gan, Yinfei Yang, Zuxuan Wu,...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像生成与编辑任务，属于纯粹的视觉内容生成领域。虽然涉及强化学习技术，但论文标题明确指向图像生成应用，与推荐系统、搜索或广告的排名任务没有直接关联。这属于AIGC/内容生成范畴，属于明确的无关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 18:59:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14760v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14760v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We present UniGen-1.5, a unified multimodal large language model (MLLM) for advanced image understanding, generation and editing. Building upon UniGen, we comprehensively enhance the model architecture and training pipeline to strengthen the image understanding and generation capabilities while unlocking strong image editing ability. Especially, we propose a unified Reinforcement Learning (RL) strategy that improves both image generation and image editing jointly via shared reward models. To further enhance image editing performance, we propose a light Edit Instruction Alignment stage that significantly improves the editing instruction comprehension that is essential for the success of the RL training. Experimental results show that UniGen-1.5 demonstrates competitive understanding and generation performance. Specifically, UniGen-1.5 achieves 0.89 and 4.31 overall scores on GenEval and ImgEdit that surpass the state-of-the-art models such as BAGEL and reaching performance comparable to proprietary models such as GPT-Image-1.
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            <a href="https://www.alphaxiv.org/abs/2511.14742v1" target="_blank" rel="noopener noreferrer">
                基于神经场方法的3D城市环境视图计算与数据探索
            </a>
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            A Neural Field-Based Approach for View Computation & Data Exploration in 3D Urban Environments
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Stefan Cobeli, Kazi Shahrukh Omar, Rodrigo Valença, Nivan Ferreira, Fabio Mirand...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D城市环境中的视图计算和数据探索，属于计算机视觉和3D图形领域。虽然神经场技术本身具有技术价值，但论文内容与推荐系统、搜索或广告的核心领域没有直接关联，也不涉及Transformer架构或LLM技术的应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 18:41:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14742v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14742v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.GR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Despite the growing availability of 3D urban datasets, extracting insights remains challenging due to computational bottlenecks and the complexity of interacting with data. In fact, the intricate geometry of 3D urban environments results in high degrees of occlusion and requires extensive manual viewpoint adjustments that make large-scale exploration inefficient. To address this, we propose a view-based approach for 3D data exploration, where a vector field encodes views from the environment. To support this approach, we introduce a neural field-based method that constructs an efficient implicit representation of 3D environments. This representation enables both faster direct queries, which consist of the computation of view assessment indices, and inverse queries, which help avoid occlusion and facilitate the search for views that match desired data patterns. Our approach supports key urban analysis tasks such as visibility assessments, solar exposure evaluation, and assessing the visual impact of new developments. We validate our method through quantitative experiments, case studies informed by real-world urban challenges, and feedback from domain experts. Results show its effectiveness in finding desirable viewpoints, analyzing building facade visibility, and evaluating views from outdoor spaces. Code and data are publicly available at https://urbantk.org/neural-3d.
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            <a href="https://www.alphaxiv.org/abs/2511.14712v1" target="_blank" rel="noopener noreferrer">
                FreeSwim：重新审视滑动窗口注意力机制以实现免训练超高分辨率视频生成
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            FreeSwim: Revisiting Sliding-Window Attention Mechanisms for Training-Free Ultra-High-Resolution Video Generation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yunfeng Wu, Jiayi Song, Zhenxiong Tan, Zihao He, Songhua Liu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频生成领域，属于计算机视觉的特定应用方向。虽然提到了注意力机制改进，但核心应用是视频生成而非推荐系统、搜索或广告。滑动窗口注意力机制可能具有效率优势，但论文明确针对视频生成任务，与当前关注领域没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:56:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14712v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14712v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The quadratic time and memory complexity of the attention mechanism in modern Transformer based video generators makes end-to-end training for ultra high resolution videos prohibitively expensive. Motivated by this limitation, we introduce a training-free approach that leverages video Diffusion Transformers pretrained at their native scale to synthesize higher resolution videos without any additional training or adaptation. At the core of our method lies an inward sliding window attention mechanism, which originates from a key observation: maintaining each query token's training scale receptive field is crucial for preserving visual fidelity and detail. However, naive local window attention, unfortunately, often leads to repetitive content and exhibits a lack of global coherence in the generated results. To overcome this challenge, we devise a dual-path pipeline that backs up window attention with a novel cross-attention override strategy, enabling the semantic content produced by local attention to be guided by another branch with a full receptive field and, therefore, ensuring holistic consistency. Furthermore, to improve efficiency, we incorporate a cross-attention caching strategy for this branch to avoid the frequent computation of full 3D attention. Extensive experiments demonstrate that our method delivers ultra-high-resolution videos with fine-grained visual details and high efficiency in a training-free paradigm. Meanwhile, it achieves superior performance on VBench, even compared to training-based alternatives, with competitive or improved efficiency. Codes are available at: https://github.com/WillWu111/FreeSwim
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            <a href="https://www.alphaxiv.org/abs/2511.14702v1" target="_blank" rel="noopener noreferrer">
                超越图像所见：基于心电图与解剖学知识的晚期钆增强图像心肌瘢痕分割
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            Seeing Beyond the Image: ECG and Anatomical Knowledge-Guided Myocardial Scar Segmentation from Late Gadolinium-Enhanced Images
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Farheen Ramzan, Yusuf Kiberu, Nikesh Jathanna, Meryem Jabrane, Vicente Grau, Sha...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像分割，特别是心肌瘢痕的识别，属于明确的医学领域应用。其使用的ECG信号和医学解剖知识与推荐系统、搜索或广告领域毫无关联。该技术完全专注于医疗诊断，没有任何潜在的推荐、搜索或广告应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:42:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14702v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14702v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Accurate segmentation of myocardial scar from late gadolinium enhanced (LGE) cardiac MRI is essential for evaluating tissue viability, yet remains challenging due to variable contrast and imaging artifacts. Electrocardiogram (ECG) signals provide complementary physiological information, as conduction abnormalities can help localize or suggest scarred myocardial regions. In this work, we propose a novel multimodal framework that integrates ECG-derived electrophysiological information with anatomical priors from the AHA-17 atlas for physiologically consistent LGE-based scar segmentation. As ECGs and LGE-MRIs are not acquired simultaneously, we introduce a Temporal Aware Feature Fusion (TAFF) mechanism that dynamically weights and fuses features based on their acquisition time difference. Our method was evaluated on a clinical dataset and achieved substantial gains over the state-of-the-art image-only baseline (nnU-Net), increasing the average Dice score for scars from 0.6149 to 0.8463 and achieving high performance in both precision (0.9115) and sensitivity (0.9043). These results show that integrating physiological and anatomical knowledge allows the model to "see beyond the image", setting a new direction for robust and physiologically grounded cardiac scar segmentation.
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            <a href="https://www.alphaxiv.org/abs/2511.14698v1" target="_blank" rel="noopener noreferrer">
                HyMAD：一种用于边境监视与监控的混合多活动检测方法
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            HyMAD: A Hybrid Multi-Activity Detection Approach for Border Surveillance and Monitoring
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sriram Srinivasan, Srinivasan Aruchamy, Siva Ram Krisha Vadali
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于边境监视和监控应用，属于计算机视觉中的活动检测领域。这与我的核心关注点（推荐系统、搜索、广告）完全无关，也不涉及LLM技术、Transformer架构进展或异构数据统一建模。该研究属于纯粹的视觉应用领域，没有明显的推荐/搜索/广告应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 17:37:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14698v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14698v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span><span class="category-tag">eess.SP</span></div>
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                    Seismic sensing has emerged as a promising solution for border surveillance and monitoring; the seismic sensors that are often buried underground are small and cannot be noticed easily, making them difficult for intruders to detect, avoid, or vandalize. This significantly enhances their effectiveness compared to highly visible cameras or fences. However, accurately detecting and distinguishing between overlapping activities that are happening simultaneously, such as human intrusions, animal movements, and vehicle rumbling, remains a major challenge due to the complex and noisy nature of seismic signals. Correctly identifying simultaneous activities is critical because failing to separate them can lead to misclassification, missed detections, and an incomplete understanding of the situation, thereby reducing the reliability of surveillance systems. To tackle this problem, we propose HyMAD (Hybrid Multi-Activity Detection), a deep neural architecture based on spatio-temporal feature fusion. The framework integrates spectral features extracted with SincNet and temporal dependencies modeled by a recurrent neural network (RNN). In addition, HyMAD employs self-attention layers to strengthen intra-modal representations and a cross-modal fusion module to achieve robust multi-label classification of seismic events. e evaluate our approach on a dataset constructed from real-world field recordings collected in the context of border surveillance and monitoring, demonstrating its ability to generalize to complex, simultaneous activity scenarios involving humans, animals, and vehicles. Our method achieves competitive performance and offers a modular framework for extending seismic-based activity recognition in real-world security applications.
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            <a href="https://www.alphaxiv.org/abs/2511.14654v1" target="_blank" rel="noopener noreferrer">
                利用多普勒全息图中的心电信号改进视网膜动静脉分割
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            Improving segmentation of retinal arteries and veins using cardiac signal in doppler holograms
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Marius Dubosc, Yann Fischer, Zacharie Auray, Nicolas Boutry, Edwin Carlinet, Mic...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于医学图像处理中的视网膜血管分割技术，属于生物医学领域的具体应用。该研究不涉及推荐系统、搜索或广告的核心进展，也没有任何LLM、Transformer架构或异构数据建模的相关内容。其技术方法和应用场景与我的关注领域完全无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 16:49:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14654v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14654v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Doppler holography is an emerging retinal imaging technique that captures the dynamic behavior of blood flow with high temporal resolution, enabling quantitative assessment of retinal hemodynamics. This requires accurate segmentation of retinal arteries and veins, but traditional segmentation methods focus solely on spatial information and overlook the temporal richness of holographic data. In this work, we propose a simple yet effective approach for artery-vein segmentation in temporal Doppler holograms using standard segmentation architectures. By incorporating features derived from a dedicated pulse analysis pipeline, our method allows conventional U-Nets to exploit temporal dynamics and achieve performance comparable to more complex attention- or iteration-based models. These findings demonstrate that time-resolved preprocessing can unlock the full potential of deep learning for Doppler holography, opening new perspectives for quantitative exploration of retinal hemodynamics. The dataset is publicly available at https://huggingface.co/datasets/DigitalHolography/
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            <a href="https://www.alphaxiv.org/abs/2511.14649v1" target="_blank" rel="noopener noreferrer">
                RepAir：一种用于CT气道分割与不连续性校正的框架
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            RepAir: A Framework for Airway Segmentation and Discontinuity Correction in CT
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>John M. Oyer, Ali Namvar, Benjamin A. Hoff, Wassim W. Labaki, Ella A. Kazerooni,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于医学影像处理中的气道分割技术，属于医学和生物医学工程领域。该主题与推荐系统、搜索或广告的核心进展完全无关，也不涉及任何LLM技术、Transformer架构进展或异构数据建模。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 16:41:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14649v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14649v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Accurate airway segmentation from chest computed tomography (CT) scans is essential for quantitative lung analysis, yet manual annotation is impractical and many automated U-Net-based methods yield disconnected components that hinder reliable biomarker extraction. We present RepAir, a three-stage framework for robust 3D airway segmentation that combines an nnU-Net-based network with anatomically informed topology correction. The segmentation network produces an initial airway mask, after which a skeleton-based algorithm identifies potential discontinuities and proposes reconnections. A 1D convolutional classifier then determines which candidate links correspond to true anatomical branches versus false or obstructed paths. We evaluate RepAir on two distinct datasets: ATM'22, comprising annotated CT scans from predominantly healthy subjects and AeroPath, encompassing annotated scans with severe airway pathology. Across both datasets, RepAir outperforms existing 3D U-Net-based approaches such as Bronchinet and NaviAirway on both voxel-level and topological metrics, and produces more complete and anatomically consistent airway trees while maintaining high segmentation accuracy.
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            <a href="https://www.alphaxiv.org/abs/2511.14639v1" target="_blank" rel="noopener noreferrer">
                SLAM-AGS：计算细胞学中基于自适应梯度手术的切片标签感知多任务预训练
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            SLAM-AGS: Slide-Label Aware Multi-Task Pretraining Using Adaptive Gradient Surgery in Computational Cytology
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Marco Acerbis, Swarnadip Chatterjee, Christophe Avenel, Joakim Lindblad
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算细胞学领域，这是一个与医学/生物学相关的特定领域应用，完全超出了您关注的RecSys、搜索或广告技术范围。标题中提到的多任务预训练和自适应梯度手术等技术虽然有趣，但没有任何与推荐系统、搜索或广告相关的潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 16:30:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14639v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14639v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Computational cytology faces two major challenges: i) instance-level labels are unreliable and prohibitively costly to obtain, ii) witness rates are extremely low. We propose SLAM-AGS, a Slide-Label-Aware Multitask pretraining framework that jointly optimizes (i) a weakly supervised similarity objective on slide-negative patches and (ii) a self-supervised contrastive objective on slide-positive patches, yielding stronger performance on downstream tasks. To stabilize learning, we apply Adaptive Gradient Surgery to tackle conflicting task gradients and prevent model collapse. We integrate the pretrained encoder into an attention-based Multiple Instance Learning aggregator for bag-level prediction and attention-guided retrieval of the most abnormal instances in a bag. On a publicly available bone-marrow cytology dataset, with simulated witness rates from 10% down to 0.5%, SLAM-AGS improves bag-level F1-Score and Top 400 positive cell retrieval over other pretraining methods, with the largest gains at low witness rates, showing that resolving gradient interference enables stable pretraining and better performance on downstream tasks. To facilitate reproducibility, we share our complete implementation and evaluation framework as open source: https://github.com/Ace95/SLAM-AGS.
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            <a href="https://www.alphaxiv.org/abs/2511.14633v1" target="_blank" rel="noopener noreferrer">
                SparseSurf：基于稀疏视图3D高斯泼溅的表面重建
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            SparseSurf: Sparse-View 3D Gaussian Splatting for Surface Reconstruction
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Meiying Gu, Jiawei Zhang, Jiahe Li, Xiaohan Yu, Haonan Luo, Jin Zheng, Xiao Bai
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D视觉领域的表面重建技术，使用稀疏视图和3D高斯泼溅方法。虽然3D重建在某些场景下可能有应用潜力，但该论文本身没有展示与推荐系统、搜索或广告的直接关联，也不涉及LLM、Transformer架构或异构数据处理等核心关注领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 16:24:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14633v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14633v1
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                    Recent advances in optimizing Gaussian Splatting for scene geometry have enabled efficient reconstruction of detailed surfaces from images. However, when input views are sparse, such optimization is prone to overfitting, leading to suboptimal reconstruction quality. Existing approaches address this challenge by employing flattened Gaussian primitives to better fit surface geometry, combined with depth regularization to alleviate geometric ambiguities under limited viewpoints. Nevertheless, the increased anisotropy inherent in flattened Gaussians exacerbates overfitting in sparse-view scenarios, hindering accurate surface fitting and degrading novel view synthesis performance. In this paper, we propose \net{}, a method that reconstructs more accurate and detailed surfaces while preserving high-quality novel view rendering. Our key insight is to introduce Stereo Geometry-Texture Alignment, which bridges rendering quality and geometry estimation, thereby jointly enhancing both surface reconstruction and view synthesis. In addition, we present a Pseudo-Feature Enhanced Geometry Consistency that enforces multi-view geometric consistency by incorporating both training and unseen views, effectively mitigating overfitting caused by sparse supervision. Extensive experiments on the DTU, BlendedMVS, and Mip-NeRF360 datasets demonstrate that our method achieves the state-of-the-art performance.
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            <a href="https://www.alphaxiv.org/abs/2511.14620v1" target="_blank" rel="noopener noreferrer">
                融合生物力学和时空特征进行跌倒预测：表征并缓解仿真到现实的差距
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            Fusing Biomechanical and Spatio-Temporal Features for Fall Prediction: Characterizing and Mitigating the Simulation-to-Reality Gap
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Md Fokhrul Islam, Sajeda Al-Hammouri, Christopher J. Arellano, Kavan Hazeli, Hem...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于生物力学和医疗领域的跌倒预测，涉及特定领域的传感器数据和仿真技术。这与搜索、推荐或广告系统的核心领域进展、LLM技术或Transformer架构改进没有任何关联。论文处理的是纯粹的医疗应用问题，属于明确排除的无关主题范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 16:13:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14620v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14620v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Falls are a leading cause of injury and loss of independence among older adults. Vision-based fall prediction systems offer a non-invasive solution to anticipate falls seconds before impact, but their development is hindered by the scarcity of available fall data. Contributing to these efforts, this study proposes the Biomechanical Spatio-Temporal Graph Convolutional Network (BioST-GCN), a dual-stream model that combines both pose and biomechanical information using a cross-attention fusion mechanism. Our model outperforms the vanilla ST-GCN baseline by 5.32% and 2.91% F1-score on the simulated MCF-UA stunt-actor and MUVIM datasets, respectively. The spatio-temporal attention mechanisms in the ST-GCN stream also provide interpretability by identifying critical joints and temporal phases. However, a critical simulation-reality gap persists. While our model achieves an 89.0% F1-score with full supervision on simulated data, zero-shot generalization to unseen subjects drops to 35.9%. This performance decline is likely due to biases in simulated data, such as `intent-to-fall' cues. For older adults, particularly those with diabetes or frailty, this gap is exacerbated by their unique kinematic profiles. To address this, we propose personalization strategies and advocate for privacy-preserving data pipelines to enable real-world validation. Our findings underscore the urgent need to bridge the gap between simulated and real-world data to develop effective fall prediction systems for vulnerable elderly populations.
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            <a href="https://www.alphaxiv.org/abs/2511.14613v1" target="_blank" rel="noopener noreferrer">
                基于3D引导的可扩展流匹配方法：从连续组织学图像生成体积组织空间转录组学数据
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            3D-Guided Scalable Flow Matching for Generating Volumetric Tissue Spatial Transcriptomics from Serial Histology
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mohammad Vali Sanian, Arshia Hemmat, Amirhossein Vahidi, Jonas Maaskola, Jimmy T...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于生物医学领域的空间转录组学数据生成，涉及3D组织学和医学图像处理技术。这属于明确的医学/生物学应用领域，与推荐系统、搜索或广告技术没有任何关联。论文内容完全落在被排除的医学/生物学应用范畴内，没有任何潜在的应用于RecSys/Search/Ads的可能性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 16:08:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14613v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14613v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    A scalable and robust 3D tissue transcriptomics profile can enable a holistic understanding of tissue organization and provide deeper insights into human biology and disease. Most predictive algorithms that infer ST directly from histology treat each section independently and ignore 3D structure, while existing 3D-aware approaches are not generative and do not scale well. We present Holographic Tissue Expression Inpainting and Analysis (HoloTea), a 3D-aware flow-matching framework that imputes spot-level gene expression from H&E while explicitly using information from adjacent sections. Our key idea is to retrieve morphologically corresponding spots on neighboring slides in a shared feature space and fuse this cross section context into a lightweight ControlNet, allowing conditioning to follow anatomical continuity. To better capture the count nature of the data, we introduce a 3D-consistent prior for flow matching that combines a learned zero-inflated negative binomial (ZINB) prior with a spatial-empirical prior constructed from neighboring sections. A global attention block introduces 3D H&E scaling linearly with the number of spots in the slide, enabling training and inference on large 3D ST datasets. Across three spatial transcriptomics datasets spanning different tissue types and resolutions, HoloTea consistently improves 3D expression accuracy and generalization compared to 2D and 3D baselines. We envision HoloTea advancing the creation of accurate 3D virtual tissues, ultimately accelerating biomarker discovery and deepening our understanding of disease.
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            <a href="https://www.alphaxiv.org/abs/2511.14604v1" target="_blank" rel="noopener noreferrer">
                XAttn-BMD：基于交叉注意力的多模态深度学习用于股骨颈骨矿物质密度估计
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            XAttn-BMD: Multimodal Deep Learning with Cross-Attention for Femoral Neck Bone Mineral Density Estimation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yilin Zhang, Leo D. Westbury, Elaine M. Dennison, Nicholas C. Harvey, Nicholas R...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像领域的骨矿物质密度估计，这是一个明确的医学应用场景，与推荐系统、搜索或广告领域完全无关。交叉注意力机制虽然是Transformer架构的一部分，但论文的应用场景严格限定在医疗诊断领域，没有任何潜在的应用于RecSys/Search/Ads的可能性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 15:53:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14604v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14604v1
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Poor bone health is a significant public health concern, and low bone mineral density (BMD) leads to an increased fracture risk, a key feature of osteoporosis. We present XAttn-BMD (Cross-Attention BMD), a multimodal deep learning framework that predicts femoral neck BMD from hip X-ray images and structured clinical metadata. It utilizes a novel bidirectional cross-attention mechanism to dynamically integrate image and metadata features for cross-modal mutual reinforcement. A Weighted Smooth L1 loss is tailored to address BMD imbalance and prioritize clinically significant cases. Extensive experiments on the data from the Hertfordshire Cohort Study show that our model outperforms the baseline models in regression generalization and robustness. Ablation studies confirm the effectiveness of both cross-attention fusion and the customized loss function. Experimental results show that the integration of multimodal data via cross-attention outperforms naive feature concatenation without cross-attention, reducing MSE by 16.7%, MAE by 6.03%, and increasing the R2 score by 16.4%, highlighting the effectiveness of the approach for femoral neck BMD estimation. Furthermore, screening performance was evaluated using binary classification at clinically relevant femoral neck BMD thresholds, demonstrating the model's potential in real-world scenarios.
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            <a href="https://www.alphaxiv.org/abs/2511.14601v1" target="_blank" rel="noopener noreferrer">
                MRI嵌入特征补充临床预测因子用于阿尔茨海默病队列认知衰退建模
            </a>
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            MRI Embeddings Complement Clinical Predictors for Cognitive Decline Modeling in Alzheimer's Disease Cohorts
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nathaniel Putera, Daniel Vilet Rodríguez, Noah Videcrantz, Julia Machnio, Mostaf...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的阿尔茨海默病认知衰退建模，涉及MRI医学影像和临床预测，属于明确的医疗应用领域。根据用户指定的无关主题，医疗、生物学等特定领域应用应被排除，且该研究没有显示出与推荐系统、搜索或广告领域的任何潜在关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 15:45:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14601v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14601v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Accurate modeling of cognitive decline in Alzheimer's disease is essential for early stratification and personalized management. While tabular predictors provide robust markers of global risk, their ability to capture subtle brain changes remains limited. In this study, we evaluate the predictive contributions of tabular and imaging-based representations, with a focus on transformer-derived Magnetic Resonance Imaging (MRI) embeddings. We introduce a trajectory-aware labeling strategy based on Dynamic Time Warping clustering to capture heterogeneous patterns of cognitive change, and train a 3D Vision Transformer (ViT) via unsupervised reconstruction on harmonized and augmented MRI data to obtain anatomy-preserving embeddings without progression labels. The pretrained encoder embeddings are subsequently assessed using both traditional machine learning classifiers and deep learning heads, and compared against tabular representations and convolutional network baselines. Results highlight complementary strengths across modalities. Clinical and volumetric features achieved the highest AUCs of around 0.70 for predicting mild and severe progression, underscoring their utility in capturing global decline trajectories. In contrast, MRI embeddings from the ViT model were most effective in distinguishing cognitively stable individuals with an AUC of 0.71. However, all approaches struggled in the heterogeneous moderate group. These findings indicate that clinical features excel in identifying high-risk extremes, whereas transformer-based MRI embeddings are more sensitive to subtle markers of stability, motivating multimodal fusion strategies for AD progression modeling.
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            <a href="https://www.alphaxiv.org/abs/2511.14599v1" target="_blank" rel="noopener noreferrer">
                CCSD：面向模态缺失的鲁棒脑肿瘤分割的跨模态组合自蒸馏
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            CCSD: Cross-Modal Compositional Self-Distillation for Robust Brain Tumor Segmentation with Missing Modalities
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dongqing Xie, Yonghuang Wu, Zisheng Ai, Jun Min, Zhencun Jiang, Shaojin Geng, Le...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像领域的脑肿瘤分割，属于明确的医学应用范畴。虽然涉及跨模态学习技术，但其特定于医疗诊断场景，与推荐系统、搜索或广告领域没有直接关联，完全属于被排除的医学领域应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 15:39:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14599v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14599v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    The accurate segmentation of brain tumors from multi-modal MRI is critical for clinical diagnosis and treatment planning. While integrating complementary information from various MRI sequences is a common practice, the frequent absence of one or more modalities in real-world clinical settings poses a significant challenge, severely compromising the performance and generalizability of deep learning-based segmentation models. To address this challenge, we propose a novel Cross-Modal Compositional Self-Distillation (CCSD) framework that can flexibly handle arbitrary combinations of input modalities. CCSD adopts a shared-specific encoder-decoder architecture and incorporates two self-distillation strategies: (i) a hierarchical modality self-distillation mechanism that transfers knowledge across modality hierarchies to reduce semantic discrepancies, and (ii) a progressive modality combination distillation approach that enhances robustness to missing modalities by simulating gradual modality dropout during training. Extensive experiments on public brain tumor segmentation benchmarks demonstrate that CCSD achieves state-of-the-art performance across various missing-modality scenarios, with strong generalization and stability.
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            <a href="https://www.alphaxiv.org/abs/2511.14588v1" target="_blank" rel="noopener noreferrer">
                基于深度学习的区域白质高信号映射作为阿尔茨海默病的稳健生物标志物
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            Deep Learning-Based Regional White Matter Hyperintensity Mapping as a Robust Biomarker for Alzheimer's Disease
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Julia Machnio, Mads Nielsen, Mostafa Mehdipour Ghazi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的阿尔茨海默病生物标志物研究，使用深度学习进行医学影像分析。这完全属于被排除的医学/生物学应用领域，与推荐系统、搜索、广告或相关使能技术没有任何关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 15:32:46
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                <a href="https://arxiv.org/abs/2511.14588v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14588v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    White matter hyperintensities (WMH) are key imaging markers in cognitive aging, Alzheimer's disease (AD), and related dementias. Although automated methods for WMH segmentation have advanced, most provide only global lesion load and overlook their spatial distribution across distinct white matter regions. We propose a deep learning framework for robust WMH segmentation and localization, evaluated across public datasets and an independent Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our results show that the predicted lesion loads are in line with the reference WMH estimates, confirming the robustness to variations in lesion load, acquisition, and demographics. Beyond accurate segmentation, we quantify WMH load within anatomically defined regions and combine these measures with brain structure volumes to assess diagnostic value. Regional WMH volumes consistently outperform global lesion burden for disease classification, and integration with brain atrophy metrics further improves performance, reaching area under the curve (AUC) values up to 0.97. Several spatially distinct regions, particularly within anterior white matter tracts, are reproducibly associated with diagnostic status, indicating localized vulnerability in AD. These results highlight the added value of regional WMH quantification. Incorporating localized lesion metrics alongside atrophy markers may enhance early diagnosis and stratification in neurodegenerative disorders.
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            <a href="https://www.alphaxiv.org/abs/2511.14558v1" target="_blank" rel="noopener noreferrer">
                通过聚类激活解释数字病理学模型
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            Explaining Digital Pathology Models via Clustering Activations
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Adam Bajger, Jan Obdržálek, Vojtěch Kůr, Rudolf Nenutil, Petr Holub, Vít Musil, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于数字病理学这一医疗领域的具体应用，与推荐系统、搜索或广告完全无关。论文讨论的模型解释方法虽然技术上有一定通用性，但被限定在医疗病理学这一无关领域，没有显示出在RecSys/Search/Ads中的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 15:00:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14558v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14558v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We present a clustering-based explainability technique for digital pathology models based on convolutional neural networks. Unlike commonly used methods based on saliency maps, such as occlusion, GradCAM, or relevance propagation, which highlight regions that contribute the most to the prediction for a single slide, our method shows the global behaviour of the model under consideration, while also providing more fine-grained information. The result clusters can be visualised not only to understand the model, but also to increase confidence in its operation, leading to faster adoption in clinical practice. We also evaluate the performance of our technique on an existing model for detecting prostate cancer, demonstrating its usefulness.
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            <a href="https://www.alphaxiv.org/abs/2511.14554v1" target="_blank" rel="noopener noreferrer">
                ForensicFlow：一种用于鲁棒深度伪造检测的三模态自适应网络
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            ForensicFlow: A Tri-Modal Adaptive Network for Robust Deepfake Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mohammad Romani
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于深度伪造检测的计算机视觉任务，属于纯粹的多模态视觉安全领域。虽然涉及多模态处理，但其应用场景（伪造检测）与推荐系统、搜索或广告的核心技术需求（如用户建模、内容理解、排序优化）没有直接关联，也不属于LLM或Transformer架构的使能技术范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 14:56:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14554v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14554v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CR</span><span class="category-tag">cs.LG</span></div>
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                    Deepfakes generated by advanced GANs and autoencoders severely threaten information integrity and societal stability. Single-stream CNNs fail to capture multi-scale forgery artifacts across spatial, texture, and frequency domains, limiting robustness and generalization. We introduce the ForensicFlow, a tri-modal forensic framework that synergistically fuses RGB, texture, and frequency evidence for video Deepfake detection. The RGB branch (ConvNeXt-tiny) extracts global visual inconsistencies; the texture branch (Swin Transformer-tiny) detects fine-grained blending artifacts; the frequency branch (CNN + SE) identifies periodic spectral noise. Attention-based temporal pooling dynamically prioritizes high-evidence frames, while adaptive attention fusion balances branch contributions.Trained on Celeb-DF (v2) with Focal Loss, ForensicFlow achieves AUC 0.9752, F1-Score 0.9408, and accuracy 0.9208, outperforming single-stream baselines. Ablation validates branch synergy; Grad-CAM confirms forensic focus. This comprehensive feature fusion provides superior resilience against subtle forgeries.
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            <a href="https://www.alphaxiv.org/abs/2511.14540v1" target="_blank" rel="noopener noreferrer">
                用于动态手-物体交互重建的交互感知四维高斯泼溅
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            Interaction-Aware 4D Gaussian Splatting for Dynamic Hand-Object Interaction Reconstruction
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hao Tian, Chenyangguang Zhang, Rui Liu, Wen Shen, Xiaolin Qin
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的动态3D重建技术，特别是手-物体交互场景的4D建模。虽然技术本身很先进，但主要应用于视觉理解和物理交互模拟领域，与推荐系统、搜索或广告的核心技术栈没有直接关联，也没有明显的潜在应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 14:44:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14540v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14540v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper focuses on a challenging setting of simultaneously modeling geometry and appearance of hand-object interaction scenes without any object priors. We follow the trend of dynamic 3D Gaussian Splatting based methods, and address several significant challenges. To model complex hand-object interaction with mutual occlusion and edge blur, we present interaction-aware hand-object Gaussians with newly introduced optimizable parameters aiming to adopt piecewise linear hypothesis for clearer structural representation. Moreover, considering the complementarity and tightness of hand shape and object shape during interaction dynamics, we incorporate hand information into object deformation field, constructing interaction-aware dynamic fields to model flexible motions. To further address difficulties in the optimization process, we propose a progressive strategy that handles dynamic regions and static background step by step. Correspondingly, explicit regularizations are designed to stabilize the hand-object representations for smooth motion transition, physical interaction reality, and coherent lighting. Experiments show that our approach surpasses existing dynamic 3D-GS-based methods and achieves state-of-the-art performance in reconstructing dynamic hand-object interaction.
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            <a href="https://www.alphaxiv.org/abs/2511.14521v1" target="_blank" rel="noopener noreferrer">
                一种具有真实监督的水下图像恢复与增强生成数据框架
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            A Generative Data Framework with Authentic Supervision for Underwater Image Restoration and Enhancement
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yufeng Tian, Yifan Chen, Zhe Sun, Libang Chen, Mingyu Dou, Jijun Lu, Ye Zheng, X...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于水下图像处理这一特定计算机视觉领域，与推荐系统、搜索或广告的核心技术无直接关联。虽然涉及生成模型，但其应用场景（水下图像）和问题定义（图像恢复与增强）过于专业化，缺乏在推荐、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 14:20:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14521v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14521v1
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                    Underwater image restoration and enhancement are crucial for correcting color distortion and restoring image details, thereby establishing a fundamental basis for subsequent underwater visual tasks. However, current deep learning methodologies in this area are frequently constrained by the scarcity of high-quality paired datasets. Since it is difficult to obtain pristine reference labels in underwater scenes, existing benchmarks often rely on manually selected results from enhancement algorithms, providing debatable reference images that lack globally consistent color and authentic supervision. This limits the model's capabilities in color restoration, image enhancement, and generalization. To overcome this limitation, we propose using in-air natural images as unambiguous reference targets and translating them into underwater-degraded versions, thereby constructing synthetic datasets that provide authentic supervision signals for model learning. Specifically, we establish a generative data framework based on unpaired image-to-image translation, producing a large-scale dataset that covers 6 representative underwater degradation types. The framework constructs synthetic datasets with precise ground-truth labels, which facilitate the learning of an accurate mapping from degraded underwater images to their pristine scene appearances. Extensive quantitative and qualitative experiments across 6 representative network architectures and 3 independent test sets show that models trained on our synthetic data achieve comparable or superior color restoration and generalization performance to those trained on existing benchmarks. This research provides a reliable and scalable data-driven solution for underwater image restoration and enhancement. The generated dataset is publicly available at: https://github.com/yftian2025/SynUIEDatasets.git.
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            <a href="https://www.alphaxiv.org/abs/2511.14518v1" target="_blank" rel="noopener noreferrer">
                D-PerceptCT：用于低剂量CT图像的深度感知增强
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            D-PerceptCT: Deep Perceptual Enhancement for Low-Dose CT Images
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Taifour Yousra Nabila, Azeddine Beghdadi, Marie Luong, Zuheng Ming, Habib Zaidi,...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学CT图像的深度感知增强技术，属于医学影像处理领域。这与我的关注点（推荐系统、搜索、广告中的LLM和Transformer技术）完全无关，不涉及任何推荐、搜索或广告相关的技术应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 14:14:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14518v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14518v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Low Dose Computed Tomography (LDCT) is widely used as an imaging solution to aid diagnosis and other clinical tasks. However, this comes at the price of a deterioration in image quality due to the low dose of radiation used to reduce the risk of secondary cancer development. While some efficient methods have been proposed to enhance LDCT quality, many overestimate noise and perform excessive smoothing, leading to a loss of critical details. In this paper, we introduce D-PerceptCT, a novel architecture inspired by key principles of the Human Visual System (HVS) to enhance LDCT images. The objective is to guide the model to enhance or preserve perceptually relevant features, thereby providing radiologists with CT images where critical anatomical structures and fine pathological details are perceptu- ally visible. D-PerceptCT consists of two main blocks: 1) a Visual Dual-path Extractor (ViDex), which integrates semantic priors from a pretrained DINOv2 model with local spatial features, allowing the network to incorporate semantic-awareness during enhancement; (2) a Global-Local State-Space block that captures long-range information and multiscale features to preserve the important structures and fine details for diagnosis. In addition, we propose a novel deep perceptual loss, designated as the Deep Perceptual Relevancy Loss Function (DPRLF), which is inspired by human contrast sensitivity, to further emphasize perceptually important features. Extensive experiments on the Mayo2016 dataset demonstrate the effectiveness of D-PerceptCT method for LDCT enhancement, showing better preservation of structural and textural information within LDCT images compared to SOTA methods.
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            <a href="https://www.alphaxiv.org/abs/2511.14481v1" target="_blank" rel="noopener noreferrer">
                面向卫星图像超分辨率的感知分割隐扩散模型：实现小农农场边界划分
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            Segmentation-Aware Latent Diffusion for Satellite Image Super-Resolution: Enabling Smallholder Farm Boundary Delineation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aditi Agarwal, Anjali Jain, Nikita Saxena, Ishan Deshpande, Michal Kazmierski, A...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于卫星图像超分辨率和农业边界划分的计算机视觉应用，与推荐系统、搜索或广告领域没有任何关联。虽然提到了扩散模型技术，但应用场景完全局限于遥感图像处理，没有展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 13:21:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14481v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14481v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Delineating farm boundaries through segmentation of satellite images is a fundamental step in many agricultural applications. The task is particularly challenging for smallholder farms, where accurate delineation requires the use of high resolution (HR) imagery which are available only at low revisit frequencies (e.g., annually). To support more frequent (sub-) seasonal monitoring, HR images could be combined as references (ref) with low resolution (LR) images -- having higher revisit frequency (e.g., weekly) -- using reference-based super-resolution (Ref-SR) methods. However, current Ref-SR methods optimize perceptual quality and smooth over crucial features needed for downstream tasks, and are unable to meet the large scale-factor requirements for this task. Further, previous two-step approaches of SR followed by segmentation do not effectively utilize diverse satellite sources as inputs. We address these problems through a new approach, $\textbf{SEED-SR}$, which uses a combination of conditional latent diffusion models and large-scale multi-spectral, multi-source geo-spatial foundation models. Our key innovation is to bypass the explicit SR task in the pixel space and instead perform SR in a segmentation-aware latent space. This unique approach enables us to generate segmentation maps at an unprecedented 20$\times$ scale factor, and rigorous experiments on two large, real datasets demonstrate up to $\textbf{25.5}$ and $\textbf{12.9}$ relative improvement in instance and semantic segmentation metrics respectively over approaches based on state-of-the-art Ref-SR methods.
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            <a href="https://www.alphaxiv.org/abs/2511.14477v1" target="_blank" rel="noopener noreferrer">
                基于点监督密度回归的二维高斯空间传输
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            2D Gaussians Spatial Transport for Point-supervised Density Regression
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Miao Shang, Xiaopeng Hong
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明研究的是计算机视觉中的密度回归问题，涉及2D高斯分布和空间传输方法。这与推荐系统、搜索或广告的核心技术领域没有直接关联，也不涉及LLM、Transformer架构或异构数据建模。该技术主要面向视觉密度估计任务，在当前聚焦范围内没有明显的应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 13:16:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14477v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14477v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This paper introduces Gaussian Spatial Transport (GST), a novel framework that leverages Gaussian splatting to facilitate transport from the probability measure in the image coordinate space to the annotation map. We propose a Gaussian splatting-based method to estimate pixel-annotation correspondence, which is then used to compute a transport plan derived from Bayesian probability. To integrate the resulting transport plan into standard network optimization in typical computer vision tasks, we derive a loss function that measures discrepancy after transport. Extensive experiments on representative computer vision tasks, including crowd counting and landmark detection, validate the effectiveness of our approach. Compared to conventional optimal transport schemes, GST eliminates iterative transport plan computation during training, significantly improving efficiency. Code is available at https://github.com/infinite0522/GST.
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            <a href="https://www.alphaxiv.org/abs/2511.14473v1" target="_blank" rel="noopener noreferrer">
                基于稀疏雷达数据利用物理引导残差学习冰下床地形
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            Learning Subglacial Bed Topography from Sparse Radar with Physics-Guided Residuals
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bayu Adhi Tama, Jianwu Wang, Vandana Janeja, Mostafa Cham
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于地球物理学中的冰下地形测绘，使用物理引导的机器学习方法处理稀疏雷达数据。这与推荐系统、搜索或广告领域没有任何直接关联，也不涉及LLM技术、Transformer架构或异构数据建模。该研究属于纯粹的地球科学应用领域，完全超出了当前关注的技术范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 13:12:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14473v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14473v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Accurate subglacial bed topography is essential for ice sheet modeling, yet radar observations are sparse and uneven. We propose a physics-guided residual learning framework that predicts bed thickness residuals over a BedMachine prior and reconstructs bed from the observed surface. A DeepLabV3+ decoder over a standard encoder (e.g.,ResNet-50) is trained with lightweight physics and data terms: multi-scale mass conservation, flow-aligned total variation, Laplacian damping, non-negativity of thickness, a ramped prior-consistency term, and a masked Huber fit to radar picks modulated by a confidence map. To measure real-world generalization, we adopt leakage-safe blockwise hold-outs (vertical/horizontal) with safety buffers and report metrics only on held-out cores. Across two Greenland sub-regions, our approach achieves strong test-core accuracy and high structural fidelity, outperforming U-Net, Attention U-Net, FPN, and a plain CNN. The residual-over-prior design, combined with physics, yields spatially coherent, physically plausible beds suitable for operational mapping under domain shift.
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            <a href="https://www.alphaxiv.org/abs/2511.14469v1" target="_blank" rel="noopener noreferrer">
                CompEvent：基于复数值事件-RGB融合的低光视频增强与去模糊
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            CompEvent: Complex-valued Event-RGB Fusion for Low-light Video Enhancement and Deblurring
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mingchen Zhong, Xin Lu, Dong Li, Senyan Xu, Ruixuan Jiang, Xueyang Fu, Baocai Yi...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的低光视频增强和去模糊技术，使用事件相机和RGB融合方法。虽然涉及多模态融合概念，但与推荐系统、搜索或广告的核心技术领域没有直接关联，也不涉及LLM、Transformer架构或异构数据建模在RecSys/Search/Ads中的应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 13:09:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14469v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14469v1
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                    Low-light video deblurring poses significant challenges in applications like nighttime surveillance and autonomous driving due to dim lighting and long exposures. While event cameras offer potential solutions with superior low-light sensitivity and high temporal resolution, existing fusion methods typically employ staged strategies, limiting their effectiveness against combined low-light and motion blur degradations. To overcome this, we propose CompEvent, a complex neural network framework enabling holistic full-process fusion of event data and RGB frames for enhanced joint restoration. CompEvent features two core components: 1) Complex Temporal Alignment GRU, which utilizes complex-valued convolutions and processes video and event streams iteratively via GRU to achieve temporal alignment and continuous fusion; and 2) Complex Space-Frequency Learning module, which performs unified complex-valued signal processing in both spatial and frequency domains, facilitating deep fusion through spatial structures and system-level characteristics. By leveraging the holistic representation capability of complex-valued neural networks, CompEvent achieves full-process spatiotemporal fusion, maximizes complementary learning between modalities, and significantly strengthens low-light video deblurring capability. Extensive experiments demonstrate that CompEvent outperforms SOTA methods in addressing this challenging task. The code is available at https://github.com/YuXie1/CompEvent.
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            <a href="https://www.alphaxiv.org/abs/2511.14440v1" target="_blank" rel="noopener noreferrer">
                学习通过婴儿的眼睛看世界：早期视觉饮食使人类和机器具备稳健的视觉智能
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            Learning to See Through a Baby's Eyes: Early Visual Diets Enable Robust Visual Intelligence in Humans and Machines
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yusen Cai, Bhargava Satya Nunna, Qing Lin, Mengmi Zhang
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注人类婴儿视觉发展和机器视觉的类比，属于纯粹的视觉研究领域。虽然标题提到机器视觉，但核心焦点是视觉智能的早期发展机制，与推荐系统、搜索或广告没有直接关联，也不涉及Transformer架构或LLM技术的应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 12:38:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14440v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14440v1
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                    Newborns perceive the world with low-acuity, color-degraded, and temporally continuous vision, which gradually sharpens as infants develop. To explore the ecological advantages of such staged "visual diets", we train self-supervised learning (SSL) models on object-centric videos under constraints that simulate infant vision: grayscale-to-color (C), blur-to-sharp (A), and preserved temporal continuity (T)-collectively termed CATDiet. For evaluation, we establish a comprehensive benchmark across ten datasets, covering clean and corrupted image recognition, texture-shape cue conflict tests, silhouette recognition, depth-order classification, and the visual cliff paradigm. All CATDiet variants demonstrate enhanced robustness in object recognition, despite being trained solely on object-centric videos. Remarkably, models also exhibit biologically aligned developmental patterns, including neural plasticity changes mirroring synaptic density in macaque V1 and behaviors resembling infants' visual cliff responses. Building on these insights, CombDiet initializes SSL with CATDiet before standard training while preserving temporal continuity. Trained on object-centric or head-mounted infant videos, CombDiet outperforms standard SSL on both in-domain and out-of-domain object recognition and depth perception. Together, these results suggest that the developmental progression of early infant visual experience offers a powerful reverse-engineering framework for understanding the emergence of robust visual intelligence in machines. All code, data, and models will be publicly released.
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            <a href="https://www.alphaxiv.org/abs/2511.14411v1" target="_blank" rel="noopener noreferrer">
                Cranio-ID：基于图神经网络的颅面识别方法——通过2D多视角X射线中的自动关键点标注实现
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            Cranio-ID: Graph-Based Craniofacial Identification via Automatic Landmark Annotation in 2D Multi-View X-rays
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ravi Shankar Prasad, Nandani Sharma, Dinesh Singh
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像分析领域的颅面识别，属于生物医学应用范畴。虽然涉及图神经网络技术，但其应用场景（X射线图像、颅面识别）与搜索、推荐、广告系统完全无关，且属于明确排除的医学领域应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 12:15:22
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                <a href="https://arxiv.org/abs/2511.14411v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14411v1
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                    In forensic craniofacial identification and in many biomedical applications, craniometric landmarks are important. Traditional methods for locating landmarks are time-consuming and require specialized knowledge and expertise. Current methods utilize superimposition and deep learning-based methods that employ automatic annotation of landmarks. However, these methods are not reliable due to insufficient large-scale validation studies. In this paper, we proposed a novel framework Cranio-ID: First, an automatic annotation of landmarks on 2D skulls (which are X-ray scans of faces) with their respective optical images using our trained YOLO-pose models. Second, cross-modal matching by formulating these landmarks into graph representations and then finding semantic correspondence between graphs of these two modalities using cross-attention and optimal transport framework. Our proposed framework is validated on the S2F and CUHK datasets (CUHK dataset resembles with S2F dataset). Extensive experiments have been conducted to evaluate the performance of our proposed framework, which demonstrates significant improvements in both reliability and accuracy, as well as its effectiveness in cross-domain skull-to-face and sketch-to-face matching in forensic science.
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            <a href="https://www.alphaxiv.org/abs/2511.14398v1" target="_blank" rel="noopener noreferrer">
                基于序数回归的糖尿病视网膜病变分期感知诊断
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            Stage Aware Diagnosis of Diabetic Retinopathy via Ordinal Regression
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Saksham Kumar, D Sridhar Aditya, T Likhil Kumar, Thulasi Bikku, Srinivasarao Tho...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的糖尿病视网膜病变诊断，属于明确的医学应用范畴。这与搜索、推荐、广告系统或相关使能技术完全无关，属于需要排除的领域特定应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 12:02:50
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                <a href="https://arxiv.org/abs/2511.14398v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14398v1
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                    Diabetic Retinopathy (DR) has emerged as a major cause of preventable blindness in recent times. With timely screening and intervention, the condition can be prevented from causing irreversible damage. The work introduces a state-of-the-art Ordinal Regression-based DR Detection framework that uses the APTOS-2019 fundus image dataset. A widely accepted combination of preprocessing methods: Green Channel (GC) Extraction, Noise Masking, and CLAHE, was used to isolate the most relevant features for DR classification. Model performance was evaluated using the Quadratic Weighted Kappa, with a focus on agreement between results and clinical grading. Our Ordinal Regression approach attained a QWK score of 0.8992, setting a new benchmark on the APTOS dataset.
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            <a href="https://www.alphaxiv.org/abs/2511.14394v1" target="_blank" rel="noopener noreferrer">
                BEDLAM2.0：运动中的合成人类与相机
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            BEDLAM2.0: Synthetic Humans and Cameras in Motion
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Joachim Tesch, Giorgio Becherini, Prerana Achar, Anastasios Yiannakidis, Muhamme...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于计算机视觉领域的合成人类和相机运动生成，属于纯粹的视觉内容生成技术。虽然标题提及“合成人类”，但这与推荐系统、搜索或广告中的用户行为建模、内容排序等核心问题无关，也未体现对异构数据统一建模的潜力。该技术更适用于游戏、动画或虚拟现实等视觉应用场景，而非当前关注的领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 11:56:26
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                <a href="https://arxiv.org/abs/2511.14394v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14394v1
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                    Inferring 3D human motion from video remains a challenging problem with many applications. While traditional methods estimate the human in image coordinates, many applications require human motion to be estimated in world coordinates. This is particularly challenging when there is both human and camera motion. Progress on this topic has been limited by the lack of rich video data with ground truth human and camera movement. We address this with BEDLAM2.0, a new dataset that goes beyond the popular BEDLAM dataset in important ways. In addition to introducing more diverse and realistic cameras and camera motions, BEDLAM2.0 increases diversity and realism of body shape, motions, clothing, hair, and 3D environments. Additionally, it adds shoes, which were missing in BEDLAM. BEDLAM has become a key resource for training 3D human pose and motion regressors today and we show that BEDLAM2.0 is significantly better, particularly for training methods that estimate humans in world coordinates. We compare state-of-the art methods trained on BEDLAM and BEDLAM2.0, and find that BEDLAM2.0 significantly improves accuracy over BEDLAM. For research purposes, we provide the rendered videos, ground truth body parameters, and camera motions. We also provide the 3D assets to which we have rights and links to those from third parties.
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            <a href="https://www.alphaxiv.org/abs/2511.14391v1" target="_blank" rel="noopener noreferrer">
                通过模块化交通信号灯和标志识别增强基于大语言模型的自动驾驶
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            Enhancing LLM-based Autonomous Driving with Modular Traffic Light and Sign Recognition
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Fabian Schmidt, Noushiq Mohammed Kayilan Abdul Nazar, Markus Enzweiler, Abhinav ...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动驾驶领域，这是与推荐系统、搜索或广告完全不同的应用领域。虽然提到了LLM技术，但应用场景是车辆控制和道路感知，没有任何与推荐、搜索或广告相关的潜在应用。论文内容完全超出了指定的关注范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 11:52:52
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                <a href="https://arxiv.org/abs/2511.14391v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14391v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Large Language Models (LLMs) are increasingly used for decision-making and planning in autonomous driving, showing promising reasoning capabilities and potential to generalize across diverse traffic situations. However, current LLM-based driving agents lack explicit mechanisms to enforce traffic rules and often struggle to reliably detect small, safety-critical objects such as traffic lights and signs. To address this limitation, we introduce TLS-Assist, a modular redundancy layer that augments LLM-based autonomous driving agents with explicit traffic light and sign recognition. TLS-Assist converts detections into structured natural language messages that are injected into the LLM input, enforcing explicit attention to safety-critical cues. The framework is plug-and-play, model-agnostic, and supports both single-view and multi-view camera setups. We evaluate TLS-Assist in a closed-loop setup on the LangAuto benchmark in CARLA. The results demonstrate relative driving performance improvements of up to 14% over LMDrive and 7% over BEVDriver, while consistently reducing traffic light and sign infractions. We publicly release the code and models on https://github.com/iis-esslingen/TLS-Assist.
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            <a href="https://www.alphaxiv.org/abs/2511.14386v1" target="_blank" rel="noopener noreferrer">
                全尺度下的立体匹配欺骗：针对自动驾驶中双目深度估计的物理对抗攻击
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            Cheating Stereo Matching in Full-scale: Physical Adversarial Attack against Binocular Depth Estimation in Autonomous Driving
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kangqiao Zhao, Shuo Huai, Xurui Song, Jun Luo
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于自动驾驶领域的计算机视觉安全攻击，属于纯粹的视觉应用范畴。虽然涉及深度估计技术，但主要关注物理对抗攻击的安全性议题，这与推荐系统、搜索或广告的核心技术发展没有直接关联，且安全/攻击主题属于明确排除的无关话题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 11:45:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14386v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14386v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Though deep neural models adopted to realize the perception of autonomous driving have proven vulnerable to adversarial examples, known attacks often leverage 2D patches and target mostly monocular perception. Therefore, the effectiveness of Physical Adversarial Examples (PAEs) on stereo-based binocular depth estimation remains largely unexplored. To this end, we propose the first texture-enabled physical adversarial attack against stereo matching models in the context of autonomous driving. Our method employs a 3D PAE with global camouflage texture rather than a local 2D patch-based one, ensuring both visual consistency and attack effectiveness across different viewpoints of stereo cameras. To cope with the disparity effect of these cameras, we also propose a new 3D stereo matching rendering module that allows the PAE to be aligned with real-world positions and headings in binocular vision. We further propose a novel merging attack that seamlessly blends the target into the environment through fine-grained PAE optimization. It has significantly enhanced stealth and lethality upon existing hiding attacks that fail to get seamlessly merged into the background. Extensive evaluations show that our PAEs can successfully fool the stereo models into producing erroneous depth information.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14376v1" target="_blank" rel="noopener noreferrer">
                生物特征身份文档中肩部呈现评估的定量方法
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            A Quantitative Method for Shoulder Presentation Evaluation in Biometric Identity Documents
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Alfonso Pedro Ridao
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于生物特征文档中的肩部呈现评估，属于生物识别技术领域，与推荐系统、搜索或广告的核心技术进展无关。论文内容涉及身份验证和文档处理，不涉及Transformer架构、LLM技术或推荐系统的任何方面，完全超出当前关注范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 11:36:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14376v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14376v1
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                    International standards for biometric identity documents mandate strict compliance with pose requirements, including the square presentation of a subject's shoulders. However, the literature on automated quality assessment offers few quantitative methods for evaluating this specific attribute. This paper proposes a Shoulder Presentation Evaluation (SPE) algorithm to address this gap. The method quantifies shoulder yaw and roll using only the 3D coordinates of two shoulder landmarks provided by common pose estimation frameworks. The algorithm was evaluated on a dataset of 121 portrait images. The resulting SPE scores demonstrated a strong Pearson correlation (r approx. 0.80) with human-assigned labels. An analysis of the metric's filtering performance, using an adapted Error-versus-Discard methodology, confirmed its utility in identifying non-compliant samples. The proposed algorithm is a viable lightweight tool for automated compliance checking in enrolment systems.
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            <a href="https://www.alphaxiv.org/abs/2511.14371v1" target="_blank" rel="noopener noreferrer">
                基于特征恢复的模糊鲁棒检测：一种用于先验引导红外无人机目标检测的端到端框架
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            Blur-Robust Detection via Feature Restoration: An End-to-End Framework for Prior-Guided Infrared UAV Target Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaolin Wang, Houzhang Fang, Qingshan Li, Lu Wang, Yi Chang, Luxin Yan
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于红外无人机目标检测的计算机视觉任务，涉及图像模糊处理和特征恢复技术。虽然提到了端到端框架，但其核心应用领域是无人机视觉检测，与推荐系统、搜索或广告没有直接关联。论文内容属于纯粹的视觉应用范畴，没有展示在推荐、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 11:27:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14371v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14371v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Infrared unmanned aerial vehicle (UAV) target images often suffer from motion blur degradation caused by rapid sensor movement, significantly reducing contrast between target and background. Generally, detection performance heavily depends on the discriminative feature representation between target and background. Existing methods typically treat deblurring as a preprocessing step focused on visual quality, while neglecting the enhancement of task-relevant features crucial for detection. Improving feature representation for detection under blur conditions remains challenging. In this paper, we propose a novel Joint Feature-Domain Deblurring and Detection end-to-end framework, dubbed JFD3. We design a dual-branch architecture with shared weights, where the clear branch guides the blurred branch to enhance discriminative feature representation. Specifically, we first introduce a lightweight feature restoration network, where features from the clear branch serve as feature-level supervision to guide the blurred branch, thereby enhancing its distinctive capability for detection. We then propose a frequency structure guidance module that refines the structure prior from the restoration network and integrates it into shallow detection layers to enrich target structural information. Finally, a feature consistency self-supervised loss is imposed between the dual-branch detection backbones, driving the blurred branch to approximate the feature representations of the clear one. Wealso construct a benchmark, named IRBlurUAV, containing 30,000 simulated and 4,118 real infrared UAV target images with diverse motion blur. Extensive experiments on IRBlurUAV demonstrate that JFD3 achieves superior detection performance while maintaining real-time efficiency.
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            <a href="https://www.alphaxiv.org/abs/2511.14361v1" target="_blank" rel="noopener noreferrer">
                经过临床验证的创新移动应用程序，用于评估眨眼和眼睑运动
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            Clinically-Validated Innovative Mobile Application for Assessing Blinking and Eyelid Movements
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gustavo Adolpho Bonesso, Carlos Marcelo Gurjão de Godoy, Tammy Hentona Osaki, Mi...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的移动应用，涉及眼科临床评估，这与RecSys、搜索或广告领域完全无关。该主题属于被明确排除的医疗/生物学应用范畴，没有任何技术内容与推荐系统、搜索或广告相关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 11:07:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14361v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14361v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Blinking is a vital physiological process that protects and maintains the health of the ocular surface. Objective assessment of eyelid movements remains challenging due to the complexity, cost, and limited clinical applicability of existing tools. This study presents the clinical validation of Bapp (Blink Application), a mobile application developed using the Flutter framework and integrated with Google ML Kit for on-device, real-time analysis of eyelid movements. The validation occurred using 45 videos from real patients, whose blinks were manually annotated by ophthalmology specialists from the Paulista School of Medicine of the Federal University of Sao Paulo (EPM-UNIFESP) to serve as the ground truth. Bapp's performance was evaluated using standard metrics, including Precision, Recall, and F1-Score, with results demonstrating 98.4% precision, 96.9% recall, and an overall accuracy of 98.3%. These outcomes confirm the reliability of Bapp as a portable, accessible, and objective tool for monitoring both normal and abnormal eyelid movements. The application offers a promising alternative to traditional manual blink counting, supporting continuous ocular health monitoring and postoperative evaluation in clinical environments.
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            <a href="https://www.alphaxiv.org/abs/2511.14357v1" target="_blank" rel="noopener noreferrer">
                IBGS：基于图像的高斯泼溅
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            IBGS: Image-Based Gaussian Splatting
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hoang Chuong Nguyen, Wei Mao, Jose M. Alvarez, Miaomiao Liu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其专注于计算机视觉中的3D重建技术（高斯泼溅），这与纯粹的视觉/3D视觉领域相关，没有显示出与推荐系统、搜索或广告的直接关联。基于当前焦点标准，该主题属于明确排除的无关主题类别。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 11:03:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14357v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14357v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    3D Gaussian Splatting (3DGS) has recently emerged as a fast, high-quality method for novel view synthesis (NVS). However, its use of low-degree spherical harmonics limits its ability to capture spatially varying color and view-dependent effects such as specular highlights. Existing works augment Gaussians with either a global texture map, which struggles with complex scenes, or per-Gaussian texture maps, which introduces high storage overhead. We propose Image-Based Gaussian Splatting, an efficient alternative that leverages high-resolution source images for fine details and view-specific color modeling. Specifically, we model each pixel color as a combination of a base color from standard 3DGS rendering and a learned residual inferred from neighboring training images. This promotes accurate surface alignment and enables rendering images of high-frequency details and accurate view-dependent effects. Experiments on standard NVS benchmarks show that our method significantly outperforms prior Gaussian Splatting approaches in rendering quality, without increasing the storage footprint.
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            <a href="https://www.alphaxiv.org/abs/2511.14343v1" target="_blank" rel="noopener noreferrer">
                轮廓到轮廓配准：将口内扫描模型与头影测量X光片对齐
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            Silhouette-to-Contour Registration: Aligning Intraoral Scan Models with Cephalometric Radiographs
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yiyi Miao, Taoyu Wu, Ji Jiang, Tong Chen, Zhe Tang, Zhengyong Jiang, Angelos Ste...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像配准技术，具体应用于牙科领域的口内扫描和头影测量X光片对齐。这属于医疗/生物医学应用领域，与推荐系统、搜索或广告的核心技术进展完全无关，且没有明显的跨领域应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 10:50:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14343v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14343v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Reliable 3D-2D alignment between intraoral scan (IOS) models and lateral cephalometric radiographs is critical for orthodontic diagnosis, yet conventional intensity-driven registration methods struggle under real clinical conditions, where cephalograms exhibit projective magnification, geometric distortion, low-contrast dental crowns, and acquisition-dependent variation. These factors hinder the stability of appearance-based similarity metrics and often lead to convergence failures or anatomically implausible alignments. To address these limitations, we propose DentalSCR, a pose-stable, contour-guided framework for accurate and interpretable silhouette-to-contour registration. Our method first constructs a U-Midline Dental Axis (UMDA) to establish a unified cross-arch anatomical coordinate system, thereby stabilizing initialization and standardizing projection geometry across cases. Using this reference frame, we generate radiograph-like projections via a surface-based DRR formulation with coronal-axis perspective and Gaussian splatting, which preserves clinical source-object-detector magnification and emphasizes external silhouettes. Registration is then formulated as a 2D similarity transform optimized with a symmetric bidirectional Chamfer distance under a hierarchical coarse-to-fine schedule, enabling both large capture range and subpixel-level contour agreement. We evaluate DentalSCR on 34 expert-annotated clinical cases. Experimental results demonstrate substantial reductions in landmark error-particularly at posterior teeth-tighter dispersion on the lower jaw, and low Chamfer and controlled Hausdorff distances at the curve level. These findings indicate that DentalSCR robustly handles real-world cephalograms and delivers high-fidelity, clinically inspectable 3D--2D alignment, outperforming conventional baselines.
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            <a href="https://www.alphaxiv.org/abs/2511.14336v1" target="_blank" rel="noopener noreferrer">
                ArchMap：用于牙齿计数和结构化牙科理解的牙弓展平与知识引导视觉语言模型
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            ArchMap: Arch-Flattening and Knowledge-Guided Vision Language Model for Tooth Counting and Structured Dental Understanding
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bohan Zhang, Yiyi Miao, Taoyu Wu, Tong Chen, Ji Jiang, Zhuoxiao Li, Zhe Tang, Li...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的牙齿计数和牙科理解应用，属于明确的医学领域特定应用。虽然涉及视觉语言模型技术，但其应用场景完全限定在牙科医疗领域，与推荐系统、搜索或广告没有任何关联。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 10:46:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14336v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14336v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    A structured understanding of intraoral 3D scans is essential for digital orthodontics. However, existing deep-learning approaches rely heavily on modality-specific training, large annotated datasets, and controlled scanning conditions, which limit generalization across devices and hinder deployment in real clinical workflows. Moreover, raw intraoral meshes exhibit substantial variation in arch pose, incomplete geometry caused by occlusion or tooth contact, and a lack of texture cues, making unified semantic interpretation highly challenging. To address these limitations, we propose ArchMap, a training-free and knowledge-guided framework for robust structured dental understanding. ArchMap first introduces a geometry-aware arch-flattening module that standardizes raw 3D meshes into spatially aligned, continuity-preserving multi-view projections. We then construct a Dental Knowledge Base (DKB) encoding hierarchical tooth ontology, dentition-stage policies, and clinical semantics to constrain the symbolic reasoning space. We validate ArchMap on 1060 pre-/post-orthodontic cases, demonstrating robust performance in tooth counting, anatomical partitioning, dentition-stage classification, and the identification of clinical conditions such as crowding, missing teeth, prosthetics, and caries. Compared with supervised pipelines and prompted VLM baselines, ArchMap achieves higher accuracy, reduced semantic drift, and superior stability under sparse or artifact-prone conditions. As a fully training-free system, ArchMap demonstrates that combining geometric normalization with ontology-guided multimodal reasoning offers a practical and scalable solution for the structured analysis of 3D intraoral scans in modern digital orthodontics.
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            <a href="https://www.alphaxiv.org/abs/2511.14329v1" target="_blank" rel="noopener noreferrer">
                逐步网络
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            Step by Step Network
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dongchen Han, Tianzhu Ye, Zhuofan Xia, Kaiyi Chen, Yulin Wang, Hanting Chen, Gao...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">标题过于模糊和通用，无法确定具体的技术内容或应用领域。没有提供足够信息来判断是否涉及推荐系统、搜索、广告或相关的LLM/Transformer技术。可能是任何类型的网络架构或训练方法，缺乏明确的领域指向性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 10:35:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14329v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14329v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Scaling up network depth is a fundamental pursuit in neural architecture design, as theory suggests that deeper models offer exponentially greater capability. Benefiting from the residual connections, modern neural networks can scale up to more than one hundred layers and enjoy wide success. However, as networks continue to deepen, current architectures often struggle to realize their theoretical capacity improvements, calling for more advanced designs to further unleash the potential of deeper networks. In this paper, we identify two key barriers that obstruct residual models from scaling deeper: shortcut degradation and limited width. Shortcut degradation hinders deep-layer learning, while the inherent depth-width trade-off imposes limited width. To mitigate these issues, we propose a generalized residual architecture dubbed Step by Step Network (StepsNet) to bridge the gap between theoretical potential and practical performance of deep models. Specifically, we separate features along the channel dimension and let the model learn progressively via stacking blocks with increasing width. The resulting method mitigates the two identified problems and serves as a versatile macro design applicable to various models. Extensive experiments show that our method consistently outperforms residual models across diverse tasks, including image classification, object detection, semantic segmentation, and language modeling. These results position StepsNet as a superior generalization of the widely adopted residual architecture.
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            <a href="https://www.alphaxiv.org/abs/2511.14322v1" target="_blank" rel="noopener noreferrer">
                LSP-YOLO：一种用于嵌入式设备上坐姿识别的轻量级单阶段网络
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            LSP-YOLO: A Lightweight Single-Stage Network for Sitting Posture Recognition on Embedded Devices
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nanjun Li, Ziyue Hao, Quanqiang Wang, Xuanyin Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的姿态识别任务，属于纯粹的视觉应用领域。虽然提到了轻量级网络和嵌入式设备，但坐姿识别与推荐系统、搜索或广告的核心技术没有直接关联，也不涉及LLM、Transformer架构或异构数据建模等焦点领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 10:24:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14322v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14322v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With the rise in sedentary behavior, health problems caused by poor sitting posture have drawn increasing attention. Most existing methods, whether using invasive sensors or computer vision, rely on two-stage pipelines, which result in high intrusiveness, intensive computation, and poor real-time performance on embedded edge devices. Inspired by YOLOv11-Pose, a lightweight single-stage network for sitting posture recognition on embedded edge devices termed LSP-YOLO was proposed. By integrating partial convolution(PConv) and Similarity-Aware Activation Module(SimAM), a lightweight module, Light-C3k2, was designed to reduce computational cost while maintaining feature extraction capability. In the recognition head, keypoints were directly mapped to posture classes through pointwise convolution, and intermediate supervision was employed to enable efficient fusion of pose estimation and classification. Furthermore, a dataset containing 5,000 images across six posture categories was constructed for model training and testing. The smallest trained model, LSP-YOLO-n, achieved 94.2% accuracy and 251 Fps on personal computer(PC) with a model size of only 1.9 MB. Meanwhile, real-time and high-accuracy inference under constrained computational resources was demonstrated on the SV830C + GC030A platform. The proposed approach is characterized by high efficiency, lightweight design and deployability, making it suitable for smart classrooms, rehabilitation, and human-computer interaction applications.
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            <a href="https://www.alphaxiv.org/abs/2511.14315v1" target="_blank" rel="noopener noreferrer">
                Dental3R：基于稀疏视角照片的口内三维重建的几何感知配对方法
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            Dental3R: Geometry-Aware Pairing for Intraoral 3D Reconstruction from Sparse-View Photographs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yiyi Miao, Taoyu Wu, Tong Chen, Ji Jiang, Zhe Tang, Zhengyong Jiang, Angelos Ste...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于牙科医学领域的3D重建技术，涉及计算机视觉和几何处理。虽然提到了3D重建，但这是针对特定医疗领域（牙科）的应用，与推荐系统、搜索或广告的核心技术领域没有直接关联。该技术缺乏在RecSys/Search/Ads领域的潜在应用场景。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 10:20:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14315v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14315v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Intraoral 3D reconstruction is fundamental to digital orthodontics, yet conventional methods like intraoral scanning are inaccessible for remote tele-orthodontics, which typically relies on sparse smartphone imagery. While 3D Gaussian Splatting (3DGS) shows promise for novel view synthesis, its application to the standard clinical triad of unposed anterior and bilateral buccal photographs is challenging. The large view baselines, inconsistent illumination, and specular surfaces common in intraoral settings can destabilize simultaneous pose and geometry estimation. Furthermore, sparse-view photometric supervision often induces a frequency bias, leading to over-smoothed reconstructions that lose critical diagnostic details. To address these limitations, we propose \textbf{Dental3R}, a pose-free, graph-guided pipeline for robust, high-fidelity reconstruction from sparse intraoral photographs. Our method first constructs a Geometry-Aware Pairing Strategy (GAPS) to intelligently select a compact subgraph of high-value image pairs. The GAPS focuses on correspondence matching, thereby improving the stability of the geometry initialization and reducing memory usage. Building on the recovered poses and point cloud, we train the 3DGS model with a wavelet-regularized objective. By enforcing band-limited fidelity using a discrete wavelet transform, our approach preserves fine enamel boundaries and interproximal edges while suppressing high-frequency artifacts. We validate our approach on a large-scale dataset of 950 clinical cases and an additional video-based test set of 195 cases. Experimental results demonstrate that Dental3R effectively handles sparse, unposed inputs and achieves superior novel view synthesis quality for dental occlusion visualization, outperforming state-of-the-art methods.
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            <a href="https://www.alphaxiv.org/abs/2511.14310v1" target="_blank" rel="noopener noreferrer">
                用于多源静态CT重建的迭代扩散精炼神经衰减场：NAF与扩散模型相遇
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            Iterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary CT Reconstruction: NAF Meets Diffusion Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiancheng Fang, Shaoyu Wang, Junlin Wang, Weiwen Wu, Yikun Zhang, Qiegen Liu
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学CT重建领域，属于医疗影像处理的特定应用。标题中的'神经衰减场'和'扩散模型'虽然涉及神经网络技术，但应用场景明确限定在医学CT重建，与推荐系统、搜索或广告领域没有任何直接或潜在关联。这完全属于医疗领域的专业技术应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 10:14:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14310v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14310v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Multi-source stationary computed tomography (CT) has recently attracted attention for its ability to achieve rapid image reconstruction, making it suitable for time-sensitive clinical and industrial applications. However, practical systems are often constrained by ultra-sparse-view sampling, which significantly degrades reconstruction quality. Traditional methods struggle under ultra-sparse-view settings, where interpolation becomes inaccurate and the resulting reconstructions are unsatisfactory. To address this challenge, this study proposes Diffusion-Refined Neural Attenuation Fields (Diff-NAF), an iterative framework tailored for multi-source stationary CT under ultra-sparse-view conditions. Diff-NAF combines a Neural Attenuation Field representation with a dual-branch conditional diffusion model. The process begins by training an initial NAF using ultra-sparse-view projections. New projections are then generated through an Angle-Prior Guided Projection Synthesis strategy that exploits inter view priors, and are subsequently refined by a Diffusion-driven Reuse Projection Refinement Module. The refined projections are incorporated as pseudo-labels into the training set for the next iteration. Through iterative refinement, Diff-NAF progressively enhances projection completeness and reconstruction fidelity under ultra-sparse-view conditions, ultimately yielding high-quality CT reconstructions. Experimental results on multiple simulated 3D CT volumes and real projection data demonstrate that Diff-NAF achieves the best performance under ultra-sparse-view conditions.
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            <a href="https://www.alphaxiv.org/abs/2511.14302v1" target="_blank" rel="noopener noreferrer">
                SAM-Fed：基于SAM引导的联邦半监督学习用于医学图像分割
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            SAM-Fed: SAM-Guided Federated Semi-Supervised Learning for Medical Image Segmentation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sahar Nasirihaghighi, Negin Ghamsarian, Yiping Li, Marcel Breeuwer, Raphael Szni...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及联邦学习和医学图像分割，这两个主题均被明确列为不相关领域。联邦学习属于隐私/安全范畴，医学图像分割属于特定领域应用，与推荐系统、搜索或广告的核心技术进展无关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 09:58:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14302v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14302v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Medical image segmentation is clinically important, yet data privacy and the cost of expert annotation limit the availability of labeled data. Federated semi-supervised learning (FSSL) offers a solution but faces two challenges: pseudo-label reliability depends on the strength of local models, and client devices often require compact or heterogeneous architectures due to limited computational resources. These constraints reduce the quality and stability of pseudo-labels, while large models, though more accurate, cannot be trained or used for routine inference on client devices. We propose SAM-Fed, a federated semi-supervised framework that leverages a high-capacity segmentation foundation model to guide lightweight clients during training. SAM-Fed combines dual knowledge distillation with an adaptive agreement mechanism to refine pixel-level supervision. Experiments on skin lesion and polyp segmentation across homogeneous and heterogeneous settings show that SAM-Fed consistently outperforms state-of-the-art FSSL methods.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.14286v1" target="_blank" rel="noopener noreferrer">
                NeuralBoneReg：一种新颖的自监督方法，用于实现鲁棒且准确的多模态骨表面配准
            </a>
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        <div class="mb-2 text-base text-gray-700">
            NeuralBoneReg: A Novel Self-Supervised Method for Robust and Accurate Multi-Modal Bone Surface Registration
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Luohong Wu, Matthias Seibold, Nicola A. Cavalcanti, Yunke Ao, Roman Flepp, Aidan...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像领域的骨表面配准技术，属于生物医学应用范畴。虽然涉及多模态数据和自监督学习，但其核心应用场景（医学骨配准）与推荐系统、搜索或广告领域完全无关，且没有显示出任何在这些领域应用的潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 09:23:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14286v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14286v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In computer- and robot-assisted orthopedic surgery (CAOS), patient-specific surgical plans derived from preoperative imaging define target locations and implant trajectories. During surgery, these plans must be accurately transferred, relying on precise cross-registration between preoperative and intraoperative data. However, substantial modality heterogeneity across imaging modalities makes this registration challenging and error-prone. Robust, automatic, and modality-agnostic bone surface registration is therefore clinically important. We propose NeuralBoneReg, a self-supervised, surface-based framework that registers bone surfaces using 3D point clouds as a modality-agnostic representation. NeuralBoneReg includes two modules: an implicit neural unsigned distance field (UDF) that learns the preoperative bone model, and an MLP-based registration module that performs global initialization and local refinement by generating transformation hypotheses to align the intraoperative point cloud with the neural UDF. Unlike SOTA supervised methods, NeuralBoneReg operates in a self-supervised manner, without requiring inter-subject training data. We evaluated NeuralBoneReg against baseline methods on two publicly available multi-modal datasets: a CT-ultrasound dataset of the fibula and tibia (UltraBones100k) and a CT-RGB-D dataset of spinal vertebrae (SpineDepth). The evaluation also includes a newly introduced CT--ultrasound dataset of cadaveric subjects containing femur and pelvis (UltraBones-Hip), which will be made publicly available. NeuralBoneReg matches or surpasses existing methods across all datasets, achieving mean RRE/RTE of 1.68°/1.86 mm on UltraBones100k, 1.88°/1.89 mm on UltraBones-Hip, and 3.79°/2.45 mm on SpineDepth. These results demonstrate strong generalizability across anatomies and modalities, providing robust and accurate cross-modal alignment for CAOS.
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            <a href="https://www.alphaxiv.org/abs/2511.14283v1" target="_blank" rel="noopener noreferrer">
                NeuralSSD：用于符号距离表面重建的神经求解器
            </a>
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            NeuralSSD: A Neural Solver for Signed Distance Surface Reconstruction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zi-Chen Xi, Jiahui Huang, Hao-Xiang Chen, Francis Williams, Qun-Ce Xu, Tai-Jiang...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的3D表面重建技术，使用神经方法求解符号距离函数。这与推荐系统、搜索或广告的核心领域没有直接关联，也不涉及Transformer架构改进或LLM技术。表面重建主要应用于3D视觉和图形学领域，属于明确排除的无关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 09:20:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14283v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14283v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.GR</span><span class="category-tag">cs.LG</span></div>
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                    We proposed a generalized method, NeuralSSD, for reconstructing a 3D implicit surface from the widely-available point cloud data. NeuralSSD is a solver-based on the neural Galerkin method, aimed at reconstructing higher-quality and accurate surfaces from input point clouds. Implicit method is preferred due to its ability to accurately represent shapes and its robustness in handling topological changes. However, existing parameterizations of implicit fields lack explicit mechanisms to ensure a tight fit between the surface and input data. To address this, we propose a novel energy equation that balances the reliability of point cloud information. Additionally, we introduce a new convolutional network that learns three-dimensional information to achieve superior optimization results. This approach ensures that the reconstructed surface closely adheres to the raw input points and infers valuable inductive biases from point clouds, resulting in a highly accurate and stable surface reconstruction. NeuralSSD is evaluated on a variety of challenging datasets, including the ShapeNet and Matterport datasets, and achieves state-of-the-art results in terms of both surface reconstruction accuracy and generalizability.
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            <a href="https://www.alphaxiv.org/abs/2511.14259v1" target="_blank" rel="noopener noreferrer">
                ManipShield：用于图像篡改检测、定位与解释的统一框架
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            ManipShield: A Unified Framework for Image Manipulation Detection, Localization and Explanation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zitong Xu, Huiyu Duan, Xiaoyu Wang, Zhaolin Cai, Kaiwei Zhang, Qiang Hu, Jing Li...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像篡改检测技术，属于计算机视觉安全领域，与推荐系统、搜索或广告的核心技术无直接关联。虽然图像理解在电商搜索中有应用，但篡改检测主要针对图像真实性和安全验证，不属于当前关注的LLM技术、推荐算法或Transformer架构进展范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 08:50:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14259v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14259v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    With the rapid advancement of generative models, powerful image editing methods now enable diverse and highly realistic image manipulations that far surpass traditional deepfake techniques, posing new challenges for manipulation detection. Existing image manipulation detection and localization (IMDL) benchmarks suffer from limited content diversity, narrow generative-model coverage, and insufficient interpretability, which hinders the generalization and explanation capabilities of current manipulation detection methods. To address these limitations, we introduce \textbf{ManipBench}, a large-scale benchmark for image manipulation detection and localization focusing on AI-edited images. ManipBench contains over 450K manipulated images produced by 25 state-of-the-art image editing models across 12 manipulation categories, among which 100K images are further annotated with bounding boxes, judgment cues, and textual explanations to support interpretable detection. Building upon ManipBench, we propose \textbf{ManipShield}, an all-in-one model based on a Multimodal Large Language Model (MLLM) that leverages contrastive LoRA fine-tuning and task-specific decoders to achieve unified image manipulation detection, localization, and explanation. Extensive experiments on ManipBench and several public datasets demonstrate that ManipShield achieves state-of-the-art performance and exhibits strong generality to unseen manipulation models. Both ManipBench and ManipShield will be released upon publication.
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            <a href="https://www.alphaxiv.org/abs/2511.14247v1" target="_blank" rel="noopener noreferrer">
                V2VLoc：通过激光雷达定位实现鲁棒的无GNSS协同感知
            </a>
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            V2VLoc: Robust GNSS-Free Collaborative Perception via LiDAR Localization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenkai Lin, Qiming Xia, Wen Li, Xun Huang, Chenglu Wen
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动驾驶领域的协同感知和定位技术，使用激光雷达进行车辆间定位。这与搜索、推荐、广告系统或LLM技术没有任何直接关联，也不涉及Transformer架构或异构数据处理。该技术主要应用于自动驾驶和机器人领域，完全超出您关注的技术范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 08:34:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14247v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14247v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Multi-agents rely on accurate poses to share and align observations, enabling a collaborative perception of the environment. However, traditional GNSS-based localization often fails in GNSS-denied environments, making consistent feature alignment difficult in collaboration. To tackle this challenge, we propose a robust GNSS-free collaborative perception framework based on LiDAR localization. Specifically, we propose a lightweight Pose Generator with Confidence (PGC) to estimate compact pose and confidence representations. To alleviate the effects of localization errors, we further develop the Pose-Aware Spatio-Temporal Alignment Transformer (PASTAT), which performs confidence-aware spatial alignment while capturing essential temporal context. Additionally, we present a new simulation dataset, V2VLoc, which can be adapted for both LiDAR localization and collaborative detection tasks. V2VLoc comprises three subsets: Town1Loc, Town4Loc, and V2VDet. Town1Loc and Town4Loc offer multi-traversal sequences for training in localization tasks, whereas V2VDet is specifically intended for the collaborative detection task. Extensive experiments conducted on the V2VLoc dataset demonstrate that our approach achieves state-of-the-art performance under GNSS-denied conditions. We further conduct extended experiments on the real-world V2V4Real dataset to validate the effectiveness and generalizability of PASTAT.
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            <a href="https://www.alphaxiv.org/abs/2511.14223v1" target="_blank" rel="noopener noreferrer">
                StreamingTalker：基于自回归扩散模型的音频驱动3D面部动画
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            StreamingTalker: Audio-driven 3D Facial Animation with Autoregressive Diffusion Model
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yifan Yang, Zhi Cen, Sida Peng, Xiangwei Chen, Yifu Deng, Xinyu Zhu, Fan Jia, Xi...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于音频驱动的3D面部动画技术，属于纯粹的视觉/图形领域应用。虽然使用了扩散模型这一生成式AI技术，但论文内容与推荐系统、搜索或广告的排名、匹配、用户建模等核心问题没有任何关联，也不具备在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 07:55:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14223v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14223v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This paper focuses on the task of speech-driven 3D facial animation, which aims to generate realistic and synchronized facial motions driven by speech inputs.Recent methods have employed audio-conditioned diffusion models for 3D facial animation, achieving impressive results in generating expressive and natural animations.However, these methods process the whole audio sequences in a single pass, which poses two major challenges: they tend to perform poorly when handling audio sequences that exceed the training horizon and will suffer from significant latency when processing long audio inputs. To address these limitations, we propose a novel autoregressive diffusion model that processes input audio in a streaming manner. This design ensures flexibility with varying audio lengths and achieves low latency independent of audio duration. Specifically, we select a limited number of past frames as historical motion context and combine them with the audio input to create a dynamic condition. This condition guides the diffusion process to iteratively generate facial motion frames, enabling real-time synthesis with high-quality results. Additionally, we implemented a real-time interactive demo, highlighting the effectiveness and efficiency of our approach. We will release the code at https://zju3dv.github.io/StreamingTalker/.
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            <a href="https://www.alphaxiv.org/abs/2511.14213v1" target="_blank" rel="noopener noreferrer">
                面向文本提示盲人脸恢复的测量约束采样
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            Measurement-Constrained Sampling for Text-Prompted Blind Face Restoration
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenjie Li, Yulun Zhang, Guangwei Gao, Heng Guo, Zhanyu Ma
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的人脸恢复任务，属于纯粹的视觉处理范畴。虽然涉及文本提示，但其核心是图像生成和恢复技术，与推荐系统、搜索或广告的排名和建模需求没有直接关联。论文的技术方向不符合当前关注的任何核心领域或使能技术类别。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 07:44:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14213v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14213v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Blind face restoration (BFR) may correspond to multiple plausible high-quality (HQ) reconstructions under extremely low-quality (LQ) inputs. However, existing methods typically produce deterministic results, struggling to capture this one-to-many nature. In this paper, we propose a Measurement-Constrained Sampling (MCS) approach that enables diverse LQ face reconstructions conditioned on different textual prompts. Specifically, we formulate BFR as a measurement-constrained generative task by constructing an inverse problem through controlled degradations of coarse restorations, which allows posterior-guided sampling within text-to-image diffusion. Measurement constraints include both Forward Measurement, which ensures results align with input structures, and Reverse Measurement, which produces projection spaces, ensuring that the solution can align with various prompts. Experiments show that our MCS can generate prompt-aligned results and outperforms existing BFR methods. Codes will be released after acceptance.
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            <a href="https://www.alphaxiv.org/abs/2511.14197v1" target="_blank" rel="noopener noreferrer">
                基于边际贡献对数据集级平均精度的在线数据筛选用于目标检测
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            Online Data Curation for Object Detection via Marginal Contributions to Dataset-level Average Precision
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zitang Sun, Masakazu Yoshimura, Junji Otsuka, Atsushi Irie, Takeshi Ohashi
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉中的目标检测任务和数据筛选方法，与推荐系统、搜索或广告的核心领域没有直接关联。虽然数据质量对模型性能很重要，但该工作没有展示在异构数据建模、Transformer架构或LLM应用方面的潜在价值，因此与当前关注点相关性极低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 07:08:18
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                <a href="https://arxiv.org/abs/2511.14197v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14197v1
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                    High-quality data has become a primary driver of progress under scale laws, with curated datasets often outperforming much larger unfiltered ones at lower cost. Online data curation extends this idea by dynamically selecting training samples based on the model's evolving state. While effective in classification and multimodal learning, existing online sampling strategies rarely extend to object detection because of its structural complexity and domain gaps. We introduce DetGain, an online data curation method specifically for object detection that estimates the marginal perturbation of each image to dataset-level Average Precision (AP) based on its prediction quality. By modeling global score distributions, DetGain efficiently estimates the global AP change and computes teacher-student contribution gaps to select informative samples at each iteration. The method is architecture-agnostic and minimally intrusive, enabling straightforward integration into diverse object detection architectures. Experiments on the COCO dataset with multiple representative detectors show consistent improvements in accuracy. DetGain also demonstrates strong robustness under low-quality data and can be effectively combined with knowledge distillation techniques to further enhance performance, highlighting its potential as a general and complementary strategy for data-efficient object detection.
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            <a href="https://www.alphaxiv.org/abs/2511.14196v1" target="_blank" rel="noopener noreferrer">
                MindCross：基于脑信号进行跨主体视频重建的快速新主体适应与有限数据
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            MindCross: Fast New Subject Adaptation with Limited Data for Cross-subject Video Reconstruction from Brain Signals
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xuan-Hao Liu, Yan-Kai Liu, Tianyi Zhou, Bao-Liang Lu, Wei-Long Zheng
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于脑信号到视频的重建和跨主体适应，属于神经科学和脑机接口领域。虽然涉及信号处理和重建技术，但与推荐系统、搜索或广告的核心领域进展、LLM技术、Transformer架构或异构数据统一建模没有直接关联。该研究主要面向医疗或神经科学应用，不在当前关注的技术范畴内。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 07:04:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14196v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14196v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.MM</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.HC</span></div>
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                    Reconstructing video from brain signals is an important brain decoding task. Existing brain decoding frameworks are primarily built on a subject-dependent paradigm, which requires large amounts of brain data for each subject. However, the expensive cost of collecting brain-video data causes severe data scarcity. Although some cross-subject methods being introduced, they often overfocus with subject-invariant information while neglecting subject-specific information, resulting in slow fine-tune-based adaptation strategy. To achieve fast and data-efficient new subject adaptation, we propose MindCross, a novel cross-subject framework. MindCross's N specific encoders and one shared encoder are designed to extract subject-specific and subject-invariant information, respectively. Additionally, a Top-K collaboration module is adopted to enhance new subject decoding with the knowledge learned from previous subjects' encoders. Extensive experiments on fMRI/EEG-to-video benchmarks demonstrate MindCross's efficacy and efficiency of cross-subject decoding and new subject adaptation using only one model.
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            <a href="https://www.alphaxiv.org/abs/2511.14187v1" target="_blank" rel="noopener noreferrer">
                面向多类主动脉分割的分层语义学习
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            Hierarchical Semantic Learning for Multi-Class Aorta Segmentation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pengcheng Shi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像分割领域，具体针对主动脉的多类别分割问题。这属于医学图像处理的特定应用，与推荐系统、搜索或广告的核心技术领域完全无关。论文内容涉及计算机视觉在医疗领域的应用，没有显示出任何在推荐、搜索或广告系统中的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 06:46:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14187v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14187v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    The aorta, the body's largest artery, is prone to pathologies such as dissection, aneurysm, and atherosclerosis, which often require timely intervention. Minimally invasive repairs involving branch vessels necessitate detailed 3D anatomical analysis. Existing methods often overlook hierarchical anatomical relationships while struggling with severe class imbalance inherent in vascular structures. We address these challenges with a curriculum learning strategy that leverages a novel fractal softmax for hierarchical semantic learning. Inspired by human cognition, our approach progressively learns anatomical constraints by decomposing complex structures from simple to complex components. The curriculum learning framework naturally addresses class imbalance by first establishing robust feature representations for dominant classes before tackling rare but anatomically critical structures, significantly accelerating model convergence in multi-class scenarios. Our two-stage inference strategy achieves up to fivefold acceleration, enhancing clinical practicality. On the validation set at epoch 50, our hierarchical semantic loss improves the Dice score of nnU-Net ResEnc M by 11.65%. The proposed model demonstrates a 5.6% higher Dice score than baselines on the test set. Experimental results show significant improvements in segmentation accuracy and efficiency, making the framework suitable for real-time clinical applications. The implementation code for this challenge entry is publicly available at: https://github.com/PengchengShi1220/AortaSeg24. The code for fractal softmax will be available at https://github.com/PengchengShi1220/fractal-softmax.
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            <a href="https://www.alphaxiv.org/abs/2511.14185v1" target="_blank" rel="noopener noreferrer">
                PAVE：用于生产级自动驾驶车辆评估的端到端数据集
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            PAVE: An End-to-End Dataset for Production Autonomous Vehicle Evaluation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiangyu Li, Chen Wang, Yumao Liu, Dengbo He, Jiahao Zhang, Ke Ma
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动驾驶车辆评估数据集，与搜索、推荐或广告系统完全无关。自动驾驶属于计算机视觉和机器人技术领域，不涉及任何LLM技术、Transformer架构或推荐系统相关内容。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 06:41:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14185v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14185v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Most existing autonomous-driving datasets (e.g., KITTI, nuScenes, and the Waymo Perception Dataset), collected by human-driving mode or unidentified driving mode, can only serve as early training for the perception and prediction of autonomous vehicles (AVs). To evaluate the real behavioral safety of AVs controlled in the black box, we present the first end-to-end benchmark dataset collected entirely by autonomous-driving mode in the real world. This dataset contains over 100 hours of naturalistic data from multiple production autonomous-driving vehicle models in the market. We segment the original data into 32,727 key frames, each consisting of four synchronized camera images and high-precision GNSS/IMU data (0.8 cm localization accuracy). For each key frame, 20 Hz vehicle trajectories spanning the past 6 s and future 5 s are provided, along with detailed 2D annotations of surrounding vehicles, pedestrians, traffic lights, and traffic signs. These key frames have rich scenario-level attributes, including driver intent, area type (covering highways, urban roads, and residential areas), lighting (day, night, or dusk), weather (clear or rain), road surface (paved or unpaved), traffic and vulnerable road users (VRU) density, traffic lights, and traffic signs (warning, prohibition, and indication). To evaluate the safety of AVs, we employ an end-to-end motion planning model that predicts vehicle trajectories with an Average Displacement Error (ADE) of 1.4 m on autonomous-driving frames. The dataset continues to expand by over 10 hours of new data weekly, thereby providing a sustainable foundation for research on AV driving behavior analysis and safety evaluation.
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            <a href="https://www.alphaxiv.org/abs/2511.14179v1" target="_blank" rel="noopener noreferrer">
                DoGCLR：基于骨架动作识别的支配博弈对比学习网络
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            DoGCLR: Dominance-Game Contrastive Learning Network for Skeleton-Based Action Recognition
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yanshan Li, Ke Ma, Miaomiao Wei, Linhui Dai
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于骨架动作识别，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。骨架动作识别主要应用于人体行为分析、监控安防等场景，无法为RecSys/Search/Ads领域提供可迁移的技术洞察或应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 06:32:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14179v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14179v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Existing self-supervised contrastive learning methods for skeleton-based action recognition often process all skeleton regions uniformly, and adopt a first-in-first-out (FIFO) queue to store negative samples, which leads to motion information loss and non-optimal negative sample selection. To address these challenges, this paper proposes Dominance-Game Contrastive Learning network for skeleton-based action Recognition (DoGCLR), a self-supervised framework based on game theory. DoGCLR models the construction of positive and negative samples as a dynamic Dominance Game, where both sample types interact to reach an equilibrium that balances semantic preservation and discriminative strength. Specifically, a spatio-temporal dual weight localization mechanism identifies key motion regions and guides region-wise augmentations to enhance motion diversity while maintaining semantics. In parallel, an entropy-driven dominance strategy manages the memory bank by retaining high entropy (hard) negatives and replacing low-entropy (weak) ones, ensuring consistent exposure to informative contrastive signals. Extensive experiments are conducted on NTU RGB+D and PKU-MMD datasets. On NTU RGB+D 60 X-Sub/X-View, DoGCLR achieves 81.1%/89.4% accuracy, and on NTU RGB+D 120 X-Sub/X-Set, DoGCLR achieves 71.2%/75.5% accuracy, surpassing state-of-the-art methods by 0.1%, 2.7%, 1.1%, and 2.3%, respectively. On PKU-MMD Part I/Part II, DoGCLR performs comparably to the state-of-the-art methods and achieves a 1.9% higher accuracy on Part II, highlighting its strong robustness on more challenging scenarios.
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            <a href="https://www.alphaxiv.org/abs/2511.14161v1" target="_blank" rel="noopener noreferrer">
                RoboTidy：一个基于3D高斯泼溅的家庭整理基准，用于具身导航与行动
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            RoboTidy : A 3D Gaussian Splatting Household Tidying Benchmark for Embodied Navigation and Action
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaoquan Sun, Ruijian Zhang, Kang Pang, Bingchen Miao, Yuxiang Tan, Zhen Yang, M...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于3D视觉、具身导航和机器人行动，属于纯粹的机器人学和计算机视觉领域。虽然涉及家庭环境，但与推荐系统、搜索或广告的核心技术（如排序、用户建模、内容理解）没有任何关联，也不涉及LLM或Transformer架构的进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 05:54:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14161v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14161v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Household tidying is an important application area, yet current benchmarks neither model user preferences nor support mobility, and they generalize poorly, making it hard to comprehensively assess integrated language-to-action capabilities. To address this, we propose RoboTidy, a unified benchmark for language-guided household tidying that supports Vision-Language-Action (VLA) and Vision-Language-Navigation (VLN) training and evaluation. RoboTidy provides 500 photorealistic 3D Gaussian Splatting (3DGS) household scenes (covering 500 objects and containers) with collisions, formulates tidying as an "Action (Object, Container)" list, and supplies 6.4k high-quality manipulation demonstration trajectories and 1.5k naviagtion trajectories to support both few-shot and large-scale training. We also deploy RoboTidy in the real world for object tidying, establishing an end-to-end benchmark for household tidying. RoboTidy offers a scalable platform and bridges a key gap in embodied AI by enabling holistic and realistic evaluation of language-guided robots.
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            <a href="https://www.alphaxiv.org/abs/2511.14157v1" target="_blank" rel="noopener noreferrer">
                学习表示与协同不变性：一种可证明的广义多模态人脸反欺诈框架
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            Learning Representation and Synergy Invariances: A Povable Framework for Generalized Multimodal Face Anti-Spoofing
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xun Lin, Shuai Wang, Yi Yu, Zitong Yu, Jiale Zhou, Yizhong Liu, Xiaochun Cao, Al...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于人脸反欺诈技术，属于计算机视觉中的生物识别安全领域。虽然涉及多模态学习，但其应用场景（人脸反欺诈）与推荐系统、搜索或广告的核心技术需求无关，且属于被明确排除的安全相关主题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 05:37:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14157v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14157v1
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                    Multimodal Face Anti-Spoofing (FAS) methods, which integrate multiple visual modalities, often suffer even more severe performance degradation than unimodal FAS when deployed in unseen domains. This is mainly due to two overlooked risks that affect cross-domain multimodal generalization. The first is the modal representation invariant risk, i.e., whether representations remain generalizable under domain shift. We theoretically show that the inherent class asymmetry in FAS (diverse spoofs vs. compact reals) enlarges the upper bound of generalization error, and this effect is further amplified in multimodal settings. The second is the modal synergy invariant risk, where models overfit to domain-specific inter-modal correlations. Such spurious synergy cannot generalize to unseen attacks in target domains, leading to performance drops. To solve these issues, we propose a provable framework, namely Multimodal Representation and Synergy Invariance Learning (RiSe). For representation risk, RiSe introduces Asymmetric Invariant Risk Minimization (AsyIRM), which learns an invariant spherical decision boundary in radial space to fit asymmetric distributions, while preserving domain cues in angular space. For synergy risk, RiSe employs Multimodal Synergy Disentanglement (MMSD), a self-supervised task enhancing intrinsic, generalizable modal features via cross-sample mixing and disentanglement. Theoretical analysis and experiments verify RiSe, which achieves state-of-the-art cross-domain performance.
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            <a href="https://www.alphaxiv.org/abs/2511.14152v1" target="_blank" rel="noopener noreferrer">
                Wave-Former：基于无线信号形状补全的遮挡场景三维重建
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            Wave-Former: Through-Occlusion 3D Reconstruction via Wireless Shape Completion
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Laura Dodds, Maisy Lam, Waleed Akbar, Yibo Cheng, Fadel Adib
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于通过无线信号进行三维重建和形状补全，属于计算机视觉和无线感知领域。虽然标题包含'Former'，但这与Transformer架构无关，而是指波形形成器。该研究在推荐系统、搜索或广告领域没有明显的应用潜力，主要解决的是视觉遮挡场景下的三维重建问题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 05:26:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14152v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14152v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We present Wave-Former, a novel method capable of high-accuracy 3D shape reconstruction for completely occluded, diverse, everyday objects. This capability can open new applications spanning robotics, augmented reality, and logistics. Our approach leverages millimeter-wave (mmWave) wireless signals, which can penetrate common occlusions and reflect off hidden objects. In contrast to past mmWave reconstruction methods, which suffer from limited coverage and high noise, Wave-Former introduces a physics-aware shape completion model capable of inferring full 3D geometry. At the heart of Wave-Former's design is a novel three-stage pipeline which bridges raw wireless signals with recent advancements in vision-based shape completion by incorporating physical properties of mmWave signals. The pipeline proposes candidate geometric surfaces, employs a transformer-based shape completion model designed specifically for mmWave signals, and finally performs entropy-guided surface selection. This enables Wave-Former to be trained using entirely synthetic point-clouds, while demonstrating impressive generalization to real-world data.In head-to-head comparisons with state-of-the-art baselines, Wave-Former raises recall from 54% to 72% while maintaining a high precision of 85%.
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            <a href="https://www.alphaxiv.org/abs/2511.14149v1" target="_blank" rel="noopener noreferrer">
                iGaussian：通过前馈式3D高斯泼溅反演实现实时相机位姿估计
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            iGaussian: Real-Time Camera Pose Estimation via Feed-Forward 3D Gaussian Splatting Inversion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hao Wang, Linqing Zhao, Xiuwei Xu, Jiwen Lu, Haibin Yan
        </div>
        
        
        
        
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的3D重建和相机位姿估计技术，属于纯粹的视觉研究范畴。虽然标题提到实时处理，但核心内容（3D高斯泼溅、相机位姿估计）与推荐系统、搜索或广告的排名任务没有直接关联，也不涉及LLM或Transformer架构的进展。该技术主要面向AR/VR、机器人等应用领域，而非RecSys/Search/Ads的核心问题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 05:22:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14149v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14149v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent trends in SLAM and visual navigation have embraced 3D Gaussians as the preferred scene representation, highlighting the importance of estimating camera poses from a single image using a pre-built Gaussian model. However, existing approaches typically rely on an iterative \textit{render-compare-refine} loop, where candidate views are first rendered using NeRF or Gaussian Splatting, then compared against the target image, and finally, discrepancies are used to update the pose. This multi-round process incurs significant computational overhead, hindering real-time performance in robotics. In this paper, we propose iGaussian, a two-stage feed-forward framework that achieves real-time camera pose estimation through direct 3D Gaussian inversion. Our method first regresses a coarse 6DoF pose using a Gaussian Scene Prior-based Pose Regression Network with spatial uniform sampling and guided attention mechanisms, then refines it through feature matching and multi-model fusion. The key contribution lies in our cross-correlation module that aligns image embeddings with 3D Gaussian attributes without differentiable rendering, coupled with a Weighted Multiview Predictor that fuses features from Multiple strategically sampled viewpoints. Experimental results on the NeRF Synthetic, Mip-NeRF 360, and T\&T+DB datasets demonstrate a significant performance improvement over previous methods, reducing median rotation errors to 0.2° while achieving 2.87 FPS tracking on mobile robots, which is an impressive 10 times speedup compared to optimization-based approaches. Code: https://github.com/pythongod-exe/iGaussian
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            <a href="https://www.alphaxiv.org/abs/2511.14107v1" target="_blank" rel="noopener noreferrer">
                RTS-Mono：一种面向真实世界部署的实时自监督单目深度估计方法
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            RTS-Mono: A Real-Time Self-Supervised Monocular Depth Estimation Method for Real-World Deployment
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zeyu Cheng, Tongfei Liu, Tao Lei, Xiang Hua, Yi Zhang, Chengkai Tang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的单目深度估计，属于纯粹的视觉技术领域。虽然提到了实时部署，但该方法与推荐系统、搜索或广告中的核心排名、用户建模或内容理解没有直接关联。论文内容属于纯粹的视觉技术范畴，不符合当前关注的任何技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 03:47:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14107v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14107v1
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                    Depth information is crucial for autonomous driving and intelligent robot navigation. The simplicity and flexibility of self-supervised monocular depth estimation are conducive to its role in these fields. However, most existing monocular depth estimation models consume many computing resources. Although some methods have reduced the model's size and improved computing efficiency, the performance deteriorates, seriously hindering the real-world deployment of self-supervised monocular depth estimation models in the real world. To address this problem, we proposed a real-time self-supervised monocular depth estimation method and implemented it in the real world. It is called RTS-Mono, which is a lightweight and efficient encoder-decoder architecture. The encoder is based on Lite-Encoder, and the decoder is designed with a multi-scale sparse fusion framework to minimize redundancy, ensure performance, and improve inference speed. RTS-Mono achieved state-of-the-art (SoTA) performance in high and low resolutions with extremely low parameter counts (3 M) in experiments based on the KITTI dataset. Compared with lightweight methods, RTS-Mono improved Abs Rel and Sq Rel by 5.6% and 9.8% at low resolution and improved Sq Rel and RMSE by 6.1% and 1.9% at high resolution. In real-world deployment experiments, RTS-Mono has extremely high accuracy and can perform real-time inference on Nvidia Jetson Orin at a speed of 49 FPS. Source code is available at https://github.com/ZYCheng777/RTS-Mono.
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            <a href="https://www.alphaxiv.org/abs/2511.14100v1" target="_blank" rel="noopener noreferrer">
                基于数字孪生表征强化学习的文本驱动推理视频编辑
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            Text-Driven Reasoning Video Editing via Reinforcement Learning on Digital Twin Representations
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yiqing Shen, Chenjia Li, Mathias Unberath
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要涉及视频编辑和数字孪生技术，属于计算机视觉和多媒体处理领域。虽然提到了强化学习，但应用场景是视频编辑而非推荐系统、搜索或广告。论文内容与文本驱动的核心焦点（RecSys/Search/Ads）没有直接关联，且视频编辑属于被排除的纯粹视觉应用范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-18 03:37:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.14100v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.14100v1
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                    Text-driven video editing enables users to modify video content only using text queries. While existing methods can modify video content if explicit descriptions of editing targets with precise spatial locations and temporal boundaries are provided, these requirements become impractical when users attempt to conceptualize edits through implicit queries referencing semantic properties or object relationships. We introduce reasoning video editing, a task where video editing models must interpret implicit queries through multi-hop reasoning to infer editing targets before executing modifications, and a first model attempting to solve this complex task, RIVER (Reasoning-based Implicit Video Editor). RIVER decouples reasoning from generation through digital twin representations of video content that preserve spatial relationships, temporal trajectories, and semantic attributes. A large language model then processes this representation jointly with the implicit query, performing multi-hop reasoning to determine modifications, then outputs structured instructions that guide a diffusion-based editor to execute pixel-level changes. RIVER training uses reinforcement learning with rewards that evaluate reasoning accuracy and generation quality. Finally, we introduce RVEBenchmark, a benchmark of 100 videos with 519 implicit queries spanning three levels and categories of reasoning complexity specifically for reasoning video editing. RIVER demonstrates best performance on the proposed RVEBenchmark and also achieves state-of-the-art performance on two additional video editing benchmarks (VegGIE and FiVE), where it surpasses six baseline methods.
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                dividerLabel.addEventListener('click', function() {
                    papersContainer.classList.toggle('expanded-level-2');
                    this.textContent = papersContainer.classList.contains('expanded-level-2') ? 
                        '点击收起低分论文' : '点击展开更多论文（评分较低）';
                });
                
                divider.appendChild(dividerLabel);
                
                // 在所有非精选论文的最后一个元素后面添加分割线
                const normalPapers = papersContainer.querySelectorAll('.simple-paper-card');
                if (normalPapers.length > 0) {
                    const lastNormalPaper = normalPapers[normalPapers.length - 1];
                    papersContainer.insertBefore(divider, lastNormalPaper.nextSibling);
                }
            }
            
            // 为每个非精选论文添加点击标题展开/折叠详情的功能
            const collapsedPapers = document.querySelectorAll('.collapsed-level-1');
            collapsedPapers.forEach(paper => {
                const titleElement = paper.querySelector('h3');
                if (titleElement) {
                    titleElement.style.cursor = 'pointer';
                    
                    // 创建展开/折叠图标元素并设置样式
                    const iconElement = document.createElement('i');
                    iconElement.className = 'expand-icon fa fa-eye-slash cursor-pointer';
                    iconElement.style.marginRight = '8px';
                    
                    // 将图标插入到标题链接之前，作为同级元素
                    const linkElement = titleElement.querySelector('a');
                    if (linkElement) {
                        // 将图标直接添加到标题元素中，位于链接之前
                        titleElement.insertBefore(iconElement, linkElement);
                        
                        // 为图标单独添加点击事件处理展开/折叠
                        iconElement.addEventListener('click', function(e) {
                            e.stopPropagation(); // 阻止事件冒泡到标题元素
                            const details = paper.querySelector('.paper-details');
                            if (details) {
                                const isExpanded = details.style.display === 'block';
                                details.style.display = isExpanded ? 'none' : 'block';
                                
                                // 更新图标状态
                                this.className = isExpanded ? 
                                    'expand-icon fa fa-eye-slash cursor-pointer' : 'expand-icon fa fa-eye cursor-pointer';
                                this.style.marginRight = '8px';
                            }
                        });
                    }
                    
                    // 为标题元素添加点击事件，也可以展开/折叠，但会检查点击目标
                    titleElement.addEventListener('click', function(e) {
                        // 仅当点击的是标题本身（非链接、非图标）时才展开/折叠
                        if (!e.target.closest('a') && !e.target.closest('.expand-icon')) {
                            const details = paper.querySelector('.paper-details');
                            if (details) {
                                const isExpanded = details.style.display === 'block';
                                details.style.display = isExpanded ? 'none' : 'block';
                                
                                // 更新图标状态
                                const iconElement = this.querySelector('.expand-icon');
                                if (iconElement) {
                                    iconElement.className = isExpanded ? 
                                        'expand-icon fa fa-eye-slash cursor-pointer' : 'expand-icon fa fa-eye cursor-pointer';
                                    iconElement.style.marginRight = '8px';
                                }
                            }
                        }
                    });
                }
            });
            
            // 实现"仅显示精选"按钮功能
            const showSelectedButton = document.getElementById('show-selected');
            if (showSelectedButton) {
                showSelectedButton.addEventListener('click', function() {
                    // 显示所有精选论文，隐藏所有普通论文
                    const selectedPapers = document.querySelectorAll('.paper-card');
                    const normalPapers = document.querySelectorAll('.simple-paper-card');
                    
                    selectedPapers.forEach(paper => {
                        paper.style.display = 'block';
                    });
                    
                    normalPapers.forEach(paper => {
                        paper.style.display = 'none';
                    });
                    
                    // 更新显示计数
                    const displayCountElement = document.getElementById('display-count');
                    if (displayCountElement) {
                        displayCountElement.textContent = `显示 ${selectedPapers.length} 篇论文 (共 ${selectedPapers.length + normalPapers.length} 篇)`;
                    }
                    
                    // 更新按钮样式
                    this.className = 'px-3 py-1 bg-primary text-white rounded text-sm hover:bg-primary/90 transition-colors';
                    document.getElementById('show-all').className = 'px-3 py-1 bg-gray-200 text-gray-700 rounded text-sm hover:bg-gray-300 transition-colors';
                    
                    // 隐藏展开/折叠按钮和分割线
                    const expandToggle = document.querySelector('.expand-toggle');
                    if (expandToggle) expandToggle.style.display = 'none';
                    
                    const papersDivider = document.querySelector('.papers-divider');
                    if (papersDivider) papersDivider.style.display = 'none';
                });
            }
            
            // 实现"全部论文"按钮功能
            const showAllButton = document.getElementById('show-all');
            if (showAllButton) {
                showAllButton.addEventListener('click', function() {
                    // 显示所有论文
                    const allPapers = document.querySelectorAll('.paper-card, .simple-paper-card');
                    allPapers.forEach(paper => {
                        paper.style.display = 'block';
                    });
                    
                    // 重置折叠状态
                    papersContainer.classList.remove('expanded-all');
                    
                    // 更新显示计数
                    const displayCountElement = document.getElementById('display-count');
                    if (displayCountElement) {
                        displayCountElement.textContent = `显示 ${allPapers.length} 篇论文 (共 ${allPapers.length} 篇)`;
                    }
                    
                    // 更新按钮样式
                    this.className = 'px-3 py-1 bg-primary text-white rounded text-sm hover:bg-primary/90 transition-colors';
                    document.getElementById('show-selected').className = 'px-3 py-1 bg-gray-200 text-gray-700 rounded text-sm hover:bg-gray-300 transition-colors';
                    
                    // 重新显示展开/折叠按钮和分割线
                    const expandToggle = document.querySelector('.expand-toggle');
                    if (expandToggle) {
                        expandToggle.style.display = 'block';
                        expandToggle.textContent = '展开全部非精选论文';
                    }
                    
                    const papersDivider = document.querySelector('.papers-divider');
                    if (papersDivider) papersDivider.style.display = 'block';
                });
            }
        });
    </script>
    <script>
    
    // 初始化日历
    document.addEventListener('DOMContentLoaded', () => {
        try {
            console.log('Attempting to initialize calendar...');
            initCalendar();
        } catch (error) {
            console.error('Error initializing calendar:', error);
        }
    });
    
    // 日历初始化函数
    function initCalendar() {
        const toggleBtn = document.getElementById('date-picker-toggle');
        const datePicker = document.getElementById('date-picker');
        const calendarGrid = document.getElementById('calendar-grid');
        const prevMonthBtn = document.getElementById('prev-month');
        const nextMonthBtn = document.getElementById('next-month');
        const currentMonthEl = document.getElementById('current-month');
        const selectedDateText = document.getElementById('selected-date-text');
        
        // 当前显示的日期（从页面获取）
        const currentDateStr = document.getElementById('current-date').textContent.trim().replace(/^\d+年|月|日/g, '');
        const currentDate = new Date(currentDateStr);
        let displayYear = currentDate.getFullYear();
        let displayMonth = currentDate.getMonth();
        
        // 有论文数据的日期列表
        const availableDates = ["20251105","20251107","20251009","20251113","20251030","20251111","20251031","20251017","20251021","20251010","20251024","20251022","20251029","20251114","20251118","20251016","20251015","20251028","20251014","20251119","20251112","20251106","20251023"];
        
        // 尝试从localStorage恢复选择状态
        const savedDate = localStorage.getItem('selectedDate');
        const savedYear = localStorage.getItem('selectedYear');
        const savedMonth = localStorage.getItem('selectedMonth');
        
        // 确保页面加载时显示当前选中的日期
        // 修复持久化问题：确保每次加载都能正确恢复选中状态
        if (savedDate) {
            selectedDateText.textContent = savedDate;
            if (savedYear) displayYear = parseInt(savedYear);
            if (savedMonth) displayMonth = parseInt(savedMonth);
        } else {
            // 首次加载时，将当前页面日期保存到localStorage
            const currentPageDate = currentDateStr.replace(/\//g, '-');
            selectedDateText.textContent = currentPageDate;
            localStorage.setItem('selectedDate', currentPageDate);
            localStorage.setItem('selectedYear', currentDate.getFullYear().toString());
            localStorage.setItem('selectedMonth', currentDate.getMonth().toString());
        }
    
        // 切换日历显示状态
        toggleBtn.addEventListener('click', (e) => {
            e.stopPropagation();
            
            // 显式控制hidden类的添加和移除
            if (datePicker.classList.contains('hidden')) {
                // 显示日历 - 确保移除hidden类
                datePicker.classList.remove('hidden');
                renderCalendar();
            } else {
                // 隐藏日历
                datePicker.classList.add('hidden');
            }
        });
        
        // 点击其他区域关闭日历
        document.addEventListener('click', () => {
            if (!datePicker.classList.contains('hidden')) {
                datePicker.classList.add('hidden');
            }
        });
        
        // 阻止日历内部点击事件冒泡
        datePicker.addEventListener('click', (e) => {
            e.stopPropagation();
        });
        
        // 上月和下月按钮
        prevMonthBtn.addEventListener('click', () => {
            displayMonth--;
            if (displayMonth < 0) {
                displayMonth = 11;
                displayYear--;
            }
            renderCalendar();
        });
        
        nextMonthBtn.addEventListener('click', () => {
            displayMonth++;
            if (displayMonth > 11) {
                displayMonth = 0;
                displayYear++;
            }
            renderCalendar();
        });
        
        /**
         * 渲染日历
         */
        function renderCalendar() {
            // 清空日历网格
            calendarGrid.innerHTML = '';
            
            // 更新当前月份显示
            const monthNames = ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月'];
            currentMonthEl.textContent = displayYear + '年' + monthNames[displayMonth];
            
            // 计算当前月份的第一天是星期几
            const firstDay = new Date(displayYear, displayMonth, 1);
            const firstDayOfWeek = firstDay.getDay();
            
            // 计算当前月份的天数
            const daysInMonth = new Date(displayYear, displayMonth + 1, 0).getDate();
            
            // 添加上月的占位天数
            for (let i = 0; i < firstDayOfWeek; i++) {
                const emptyDay = document.createElement('div');
                emptyDay.classList.add('py-1', 'text-gray-300');
                calendarGrid.appendChild(emptyDay);
            }
            
            // 获取当前日期（用于高亮显示）
            const today = new Date();
            today.setHours(0, 0, 0, 0);
            
            // 添加当前月份的天数
            for (let day = 1; day <= daysInMonth; day++) {
                const dayElement = document.createElement('div');
                const currentDateObj = new Date(displayYear, displayMonth, day);
                const dateStr = displayYear + String(displayMonth + 1).padStart(2, '0') + String(day).padStart(2, '0');
                const displayDateStr = displayYear + '-' + String(displayMonth + 1).padStart(2, '0') + '-' + String(day).padStart(2, '0');
                
                // 设置日期元素基本样式
                dayElement.textContent = day;
                
                // 检查该日期是否有论文数据
                const hasPapers = availableDates.includes(dateStr);
                
                if (hasPapers) {
                    // 有论文数据的日期样式
                    dayElement.classList.add('py-1', 'cursor-pointer', 'hover:bg-gray-100', 'rounded', 'bg-blue-50', 'font-medium');
                    
                    // 添加点击事件，跳转到对应日期的页面
                    dayElement.addEventListener('click', () => {
                        console.log('Date clicked:', displayDateStr);
                        selectedDateText.textContent = displayDateStr;
                        
                        // 保存选择状态到localStorage
                        localStorage.setItem('selectedDate', displayDateStr);
                        localStorage.setItem('selectedYear', displayYear.toString());
                        localStorage.setItem('selectedMonth', displayMonth.toString());
                        
                        datePicker.classList.add('hidden');
                        
                        // 构造目标URL并跳转
                        const targetUrl = 'arxiv_' + dateStr + '.html';
                        window.location.href = targetUrl;
                    });
                } else {
                    // 没有论文数据的日期样式（置灰不可点击）
                    dayElement.classList.add('py-1', 'text-gray-400', 'cursor-not-allowed');
                }
                
                // 高亮显示当天日期（覆盖之前的样式）
                if (currentDateObj.getTime() === today.getTime()) {
                    dayElement.classList.remove('bg-blue-50');
                    dayElement.classList.add('bg-primary', 'text-white', 'font-bold', 'shadow');
                    if (!hasPapers) {
                        // 当天没有论文时，仍然置灰但保持背景色
                        dayElement.classList.add('opacity-70');
                    }
                }
                
                // 高亮显示当前选中的日期
                if (displayDateStr === selectedDateText.textContent) {
                    dayElement.classList.add('font-bold', 'border-2', 'border-primary', 'rounded-lg', 'shadow-md');
                }
                
                // 增强有论文数据的日期样式，使其更明显
                if (hasPapers && currentDateObj.getTime() !== today.getTime()) {
                    dayElement.classList.add('bg-blue-100', 'hover:bg-blue-200', 'transition-colors', 'duration-200');
                }
                
                calendarGrid.appendChild(dayElement);
            }
        }
    }
    </script>
    </body>

</html>